Phenotypic Analyses and Genetic Mapping for Cassava Population
Training on Quantitative Genetics – CIAT Cassava Breeding Team
Guilherme Pereira and João Ricardo Rosa
2023-11-16
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Introduction
This tutorial reports the phenotypic data analysis and genetic mapping for a Cassava population, which is part of the mapping current studies developed by the CIAT Breeding Program, Colombia. Our aim is to use this population to give a training on quantitative genetics to the CIAT Breeding Team. In general, the used population consists of 381 individuals belonging to five fullsib families, which were phenotyped for cooking quality traits. A total of seven parents were crossed to generate these five families: (i) SM3759-36 x VEN25; (ii) COL2246 x COL1722; (iii) COL1910 x SM3759-36; (iv) COL1910 x COL1505 (and its reciprocal COL1505 x COL1910); and (v) VEN208 x VEN25. Of the total number of individuals, four parents and 226 progenies were genotyped for 8,590,486 single nucleotide polymorphism (SNP) markers, distributed in the entire genome (18 chromosomes + scaffolds). Actually, this amount of markers were obtained for the considered population and also for a second population (370 individuals), which has been studied with the focus on beta-carotene trait. The latter population will not be analyzed in the present training, and its results will be discussed in specific meetings.
The present training is structured as follows: (i) phenotypic analysis of quality traits via mixed models, using three specific traits (DM_NIRS, WAB_20min_mean, and WAB_30min_mean) evaluated in four trials during years 2020 and 2021; (ii) construction of genetic linkage map for one of the five fullsib families (i.e, SM3759-36 x VEN25), which presented the larger amount of genotyped progenies (\(n=49\)) and both parents genotyped among all families; (iii) quantitative trait loci (QTL) mapping, via composite interval mapping (CIM), for the three considered cooking quality traits; and (iv) genome-wide association study (GWAS) for the same traits, considering all the five fullsib families and not only one of them. To perform mapping, we will explore a set of 9,000 SNP markers randomly collected from the total of markers obtained via genotyping.
Phenotypic Analyses via Mixed Models
Data and Exploratory Analysis
## plot_name trial_harvest plot_number rep_number
## 1 202002CQQU1_ciat_rep1_GM13159-1_1 D1_1st 1 1
## 2 202002CQQU1_ciat_rep1_GM13159-4_2 D1_1st 2 1
## 3 202002CQQU1_ciat_rep1_GM13159-6_3 D1_1st 3 1
## 4 202002CQQU1_ciat_rep1_GM13159-7_4 D1_1st 4 1
## 5 202002CQQU1_ciat_rep1_GM13159-8_5 D1_1st 5 1
## 6 202002CQQU1_ciat_rep1_GM13159-9_6 D1_1st 6 1
## accession_name col_number row_number check number_germinated number_planted
## 1 GM13159-1 1 1 NA 6 6
## 2 GM13159-4 1 2 NA 5 4
## 3 GM13159-6 1 3 NA 6 6
## 4 GM13159-7 1 4 NA 4 4
## 5 GM13159-8 1 5 NA 6 6
## 6 GM13159-9 1 6 NA 5 5
## germ_perc lodging vigor number_branch plant_type root_color root_type
## 1 100 1 NA 3 3 3 3
## 2 125 1 NA 4 3 3 3
## 3 100 1 NA 2 4 3 2
## 4 100 1 NA 4 3 3 2
## 5 100 1 NA 3 3 3 2
## 6 100 1 NA 2 4 3 3
## DM_NIRS DM_oven_1 DM_oven_2 DM_oven_mean HCN_rep1 HCN_rep2 HCN_mean
## 1 40.97823 41.34744 41.46142 41.40443 NA NA NA
## 2 41.77826 42.61983 42.56000 42.58992 NA NA NA
## 3 45.29284 43.47788 43.66805 43.57296 NA NA NA
## 4 43.35637 42.63282 42.66809 42.65046 NA NA NA
## 5 43.76374 42.55326 43.01971 42.78648 NA NA NA
## 6 43.24713 42.48462 42.81179 42.64821 NA NA NA
## WAB_20min_rep1 WAB_20min_rep2 WAB_20min_mean WAB_30min_rep1 WAB_30min_rep2
## 1 1.454041 1.746310 1.600175 3.451960 5.019413
## 2 1.811940 2.430337 2.121139 3.280565 3.582652
## 3 4.184988 4.113949 4.149469 12.017386 13.360680
## 4 2.244308 2.270785 2.257546 5.345699 5.129318
## 5 2.515546 2.367617 2.441581 5.016959 4.175153
## 6 2.202935 2.200254 2.201595 5.122711 4.359793
## WAB_30min_mean
## 1 4.235686
## 2 3.431608
## 3 12.689033
## 4 5.237508
## 5 4.596056
## 6 4.741252## 'data.frame': 1468 obs. of 30 variables:
## $ plot_name : chr "202002CQQU1_ciat_rep1_GM13159-1_1" "202002CQQU1_ciat_rep1_GM13159-4_2" "202002CQQU1_ciat_rep1_GM13159-6_3" "202002CQQU1_ciat_rep1_GM13159-7_4" ...
## $ trial_harvest : chr "D1_1st" "D1_1st" "D1_1st" "D1_1st" ...
## $ plot_number : int 1 2 3 4 5 6 7 8 9 10 ...
## $ rep_number : int 1 1 1 1 1 1 1 1 1 1 ...
## $ accession_name : chr "GM13159-1" "GM13159-4" "GM13159-6" "GM13159-7" ...
## $ col_number : int 1 1 1 1 1 1 1 1 2 2 ...
## $ row_number : int 1 2 3 4 5 6 7 8 8 7 ...
## $ check : int NA NA NA NA NA NA NA NA NA 1 ...
## $ number_germinated: int 6 5 6 4 6 5 5 6 6 3 ...
## $ number_planted : int 6 4 6 4 6 5 6 6 6 6 ...
## $ germ_perc : num 100 125 100 100 100 ...
## $ lodging : int 1 1 1 1 1 1 1 1 1 1 ...
## $ vigor : int NA NA NA NA NA NA NA NA NA NA ...
## $ number_branch : int 3 4 2 4 3 2 3 4 2 2 ...
## $ plant_type : int 3 3 4 3 3 4 4 4 3 5 ...
## $ root_color : int 3 3 3 3 3 3 2 3 3 3 ...
## $ root_type : int 3 3 2 2 2 3 2 3 3 5 ...
## $ DM_NIRS : num 41 41.8 45.3 43.4 43.8 ...
## $ DM_oven_1 : num 41.3 42.6 43.5 42.6 42.6 ...
## $ DM_oven_2 : num 41.5 42.6 43.7 42.7 43 ...
## $ DM_oven_mean : num 41.4 42.6 43.6 42.7 42.8 ...
## $ HCN_rep1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ HCN_rep2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ HCN_mean : num NA NA NA NA NA NA NA NA NA NA ...
## $ WAB_20min_rep1 : num 1.45 1.81 4.18 2.24 2.52 ...
## $ WAB_20min_rep2 : num 1.75 2.43 4.11 2.27 2.37 ...
## $ WAB_20min_mean : num 1.6 2.12 4.15 2.26 2.44 ...
## $ WAB_30min_rep1 : num 3.45 3.28 12.02 5.35 5.02 ...
## $ WAB_30min_rep2 : num 5.02 3.58 13.36 5.13 4.18 ...
## $ WAB_30min_mean : num 4.24 3.43 12.69 5.24 4.6 ...data.pheno <- data.pheno %>% arrange(trial_harvest, rep_number, col_number, row_number, accession_name)
factors <- c("trial_harvest", "rep_number", "accession_name")
data.pheno[,factors] <- lapply(data.pheno[,factors], factor)
str(data.pheno)## 'data.frame': 1468 obs. of 30 variables:
## $ plot_name : chr "202002CQQU1_ciat_rep1_GM13159-1_1" "202002CQQU1_ciat_rep1_GM13159-4_2" "202002CQQU1_ciat_rep1_GM13159-6_3" "202002CQQU1_ciat_rep1_GM13159-7_4" ...
## $ trial_harvest : Factor w/ 4 levels "D1_1st","D1_2nd",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ plot_number : int 1 2 3 4 5 6 7 8 16 15 ...
## $ rep_number : Factor w/ 9 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ accession_name : Factor w/ 390 levels "COL1505","COL1722",..: 6 36 55 56 57 58 8 9 16 15 ...
## $ col_number : int 1 1 1 1 1 1 1 1 2 2 ...
## $ row_number : int 1 2 3 4 5 6 7 8 1 2 ...
## $ check : int NA NA NA NA NA NA NA NA NA NA ...
## $ number_germinated: int 6 5 6 4 6 5 5 6 6 5 ...
## $ number_planted : int 6 4 6 4 6 5 6 6 6 6 ...
## $ germ_perc : num 100 125 100 100 100 ...
## $ lodging : int 1 1 1 1 1 1 1 1 1 1 ...
## $ vigor : int NA NA NA NA NA NA NA NA NA NA ...
## $ number_branch : int 3 4 2 4 3 2 3 4 4 3 ...
## $ plant_type : int 3 3 4 3 3 4 4 4 3 3 ...
## $ root_color : int 3 3 3 3 3 3 2 3 3 3 ...
## $ root_type : int 3 3 2 2 2 3 2 3 3 3 ...
## $ DM_NIRS : num 41 41.8 45.3 43.4 43.8 ...
## $ DM_oven_1 : num 41.3 42.6 43.5 42.6 42.6 ...
## $ DM_oven_2 : num 41.5 42.6 43.7 42.7 43 ...
## $ DM_oven_mean : num 41.4 42.6 43.6 42.7 42.8 ...
## $ HCN_rep1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ HCN_rep2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ HCN_mean : num NA NA NA NA NA NA NA NA NA NA ...
## $ WAB_20min_rep1 : num 1.45 1.81 4.18 2.24 2.52 ...
## $ WAB_20min_rep2 : num 1.75 2.43 4.11 2.27 2.37 ...
## $ WAB_20min_mean : num 1.6 2.12 4.15 2.26 2.44 ...
## $ WAB_30min_rep1 : num 3.45 3.28 12.02 5.35 5.02 ...
## $ WAB_30min_rep2 : num 5.02 3.58 13.36 5.13 4.18 ...
## $ WAB_30min_mean : num 4.24 3.43 12.69 5.24 4.6 ...##
## D1_1st D1_2nd P1_1st P1_2nd
## 334 334 400 400## [1] "plot_name" "trial_harvest" "plot_number"
## [4] "rep_number" "accession_name" "col_number"
## [7] "row_number" "check" "number_germinated"
## [10] "number_planted" "germ_perc" "lodging"
## [13] "vigor" "number_branch" "plant_type"
## [16] "root_color" "root_type" "DM_NIRS"
## [19] "DM_oven_1" "DM_oven_2" "DM_oven_mean"
## [22] "HCN_rep1" "HCN_rep2" "HCN_mean"
## [25] "WAB_20min_rep1" "WAB_20min_rep2" "WAB_20min_mean"
## [28] "WAB_30min_rep1" "WAB_30min_rep2" "WAB_30min_mean"## [1] "number_germinated" "number_planted" "germ_perc"
## [4] "lodging" "vigor" "number_branch"
## [7] "plant_type" "root_color" "root_type"
## [10] "DM_NIRS" "DM_oven_1" "DM_oven_2"
## [13] "DM_oven_mean" "HCN_rep1" "HCN_rep2"
## [16] "HCN_mean" "WAB_20min_rep1" "WAB_20min_rep2"
## [19] "WAB_20min_mean" "WAB_30min_rep1" "WAB_30min_rep2"
## [22] "WAB_30min_mean"Let’s take a look of checks used in the trials:
##
## GM13159-10 GM13159-15 GM13159-16 GM13159-2 GM13159-29 GM13159-3
## 2 2 2 2 2 2
## GM13159-31 GM13159-34 GM13159-35 GM13159-37 GM13159-41 GM13159-42
## 2 2 2 2 2 2
## GM13159-44 GM13159-45 GM13159-48 GM13159-5 GM13159-52 GM13169-1
## 2 2 2 2 2 2
## GM13169-15 GM13169-20 GM13169-29 GM13169-4 GM13169-5 GM13174-25
## 2 2 2 2 2 2
## GM13174-26 GM13174-52 GM13174-8 GM13175-10 GM13175-12 GM13175-13
## 2 2 2 2 2 2
## GM13175-14 GM13175-15 GM13175-17 GM13175-2 GM13175-20 GM13175-21
## 2 2 2 2 2 2
## GM13175-23 GM13175-24 GM13175-31 GM13175-32 GM13175-33 GM13175-35
## 2 2 2 2 2 2
## GM13175-36 GM13175-39 GM13175-47 GM13175-49 GM13196-1 GM13196-100
## 2 2 2 2 2 2
## GM13196-101 GM13196-102 GM13196-104 GM13196-106 GM13196-109 GM13196-110
## 2 2 2 2 2 2
## GM13196-112 GM13196-113 GM13196-115 GM13196-116 GM13196-121 GM13196-123
## 2 2 2 2 2 2
## GM13196-125 GM13196-14 GM13196-140 GM13196-143 GM13196-147 GM13196-148
## 2 2 2 2 2 2
## GM13196-149 GM13196-150 GM13196-151 GM13196-155 GM13196-156 GM13196-160
## 2 2 2 2 2 2
## GM13196-163 GM13196-21 GM13196-23 GM13196-24 GM13196-26 GM13196-32
## 2 2 2 2 2 2
## GM13196-42 GM13196-43 GM13196-44 GM13196-45 GM13196-46 GM13196-47
## 2 2 2 2 2 2
## GM13196-48 GM13196-49 GM13196-5 GM13196-53 GM13196-54 GM13196-56
## 2 2 2 2 2 2
## GM13196-57 GM13196-58 GM13196-59 GM13196-61 GM13196-64 GM13196-65
## 2 2 2 2 2 2
## GM13196-66 GM13196-70 GM13196-72 GM13196-76 GM13196-77 GM13196-80
## 2 2 2 2 2 2
## GM13196-81 GM13196-83 GM13196-85 GM13196-87 GM13196-88 GM13196-89
## 2 2 2 2 2 2
## GM13196-90 GM13196-91 GM13196-92 GM13196-93 GM13196-94 GM13196-95
## 2 2 2 2 2 2
## GM13196-96 GM13196-97 GM13196-98 GM13240-10 GM13240-11 GM13240-12
## 2 2 2 2 2 2
## GM13240-16 GM13240-20 GM13240-22 GM13240-31 GM13240-35 GM13240-36
## 2 2 2 2 2 2
## GM13240-37 GM13240-45 GM13240-5 GM13240-57 GM13240-59 GM13240-61
## 2 2 2 2 2 2
## GM13240-7 GM13240-9 GM13159-1 GM13159-11 GM13159-12 GM13159-13
## 2 2 4 4 4 4
## GM13159-14 GM13159-17 GM13159-18 GM13159-19 GM13159-20 GM13159-21
## 4 4 4 4 4 4
## GM13159-22 GM13159-23 GM13159-24 GM13159-25 GM13159-26 GM13159-27
## 4 4 4 4 4 4
## GM13159-28 GM13159-30 GM13159-32 GM13159-33 GM13159-4 GM13159-43
## 4 4 4 4 4 4
## GM13159-46 GM13159-47 GM13159-49 GM13159-50 GM13159-51 GM13159-53
## 4 4 4 4 4 4
## GM13159-54 GM13159-55 GM13159-56 GM13159-57 GM13159-6 GM13159-7
## 4 4 4 4 4 4
## GM13159-8 GM13159-9 GM13169-10 GM13169-11 GM13169-12 GM13169-13
## 4 4 4 4 4 4
## GM13169-14 GM13169-16 GM13169-18 GM13169-19 GM13169-2 GM13169-21
## 4 4 4 4 4 4
## GM13169-22 GM13169-23 GM13169-24 GM13169-25 GM13169-26 GM13169-7
## 4 4 4 4 4 4
## GM13169-8 GM13169-9 GM13174-1 GM13174-10 GM13174-11 GM13174-12
## 4 4 4 4 4 4
## GM13174-13 GM13174-14 GM13174-15 GM13174-16 GM13174-17 GM13174-18
## 4 4 4 4 4 4
## GM13174-2 GM13174-20 GM13174-21 GM13174-22 GM13174-23 GM13174-24
## 4 4 4 4 4 4
## GM13174-27 GM13174-28 GM13174-29 GM13174-3 GM13174-30 GM13174-31
## 4 4 4 4 4 4
## GM13174-32 GM13174-33 GM13174-34 GM13174-35 GM13174-36 GM13174-37
## 4 4 4 4 4 4
## GM13174-38 GM13174-39 GM13174-4 GM13174-40 GM13174-41 GM13174-42
## 4 4 4 4 4 4
## GM13174-43 GM13174-44 GM13174-45 GM13174-46 GM13174-47 GM13174-48
## 4 4 4 4 4 4
## GM13174-49 GM13174-50 GM13174-51 GM13174-53 GM13174-54 GM13174-55
## 4 4 4 4 4 4
## GM13174-56 GM13174-6 GM13174-7 GM13174-9 GM13175-1 GM13175-16
## 4 4 4 4 4 4
## GM13175-19 GM13175-22 GM13175-25 GM13175-26 GM13175-27 GM13175-28
## 4 4 4 4 4 4
## GM13175-29 GM13175-3 GM13175-30 GM13175-38 GM13175-4 GM13175-40
## 4 4 4 4 4 4
## GM13175-42 GM13175-43 GM13175-44 GM13175-45 GM13175-46 GM13175-48
## 4 4 4 4 4 4
## GM13175-5 GM13175-6 GM13175-7 GM13175-8 GM13175-9 GM13196-10
## 4 4 4 4 4 4
## GM13196-105 GM13196-107 GM13196-111 GM13196-118 GM13196-119 GM13196-12
## 4 4 4 4 4 4
## GM13196-120 GM13196-122 GM13196-124 GM13196-126 GM13196-127 GM13196-128
## 4 4 4 4 4 4
## GM13196-129 GM13196-13 GM13196-130 GM13196-131 GM13196-132 GM13196-133
## 4 4 4 4 4 4
## GM13196-134 GM13196-135 GM13196-136 GM13196-137 GM13196-138 GM13196-139
## 4 4 4 4 4 4
## GM13196-141 GM13196-142 GM13196-145 GM13196-152 GM13196-153 GM13196-154
## 4 4 4 4 4 4
## GM13196-157 GM13196-158 GM13196-159 GM13196-16 GM13196-161 GM13196-162
## 4 4 4 4 4 4
## GM13196-164 GM13196-165 GM13196-166 GM13196-2 GM13196-20 GM13196-27
## 4 4 4 4 4 4
## GM13196-28 GM13196-29 GM13196-3 GM13196-30 GM13196-31 GM13196-33
## 4 4 4 4 4 4
## GM13196-35 GM13196-36 GM13196-37 GM13196-38 GM13196-39 GM13196-4
## 4 4 4 4 4 4
## GM13196-41 GM13196-52 GM13196-60 GM13196-62 GM13196-67 GM13196-68
## 4 4 4 4 4 4
## GM13196-7 GM13196-73 GM13196-74 GM13196-75 GM13196-78 GM13196-79
## 4 4 4 4 4 4
## GM13196-8 GM13196-82 GM13196-84 GM13196-9 GM13196-99 GM13240-1
## 4 4 4 4 4 4
## GM13240-13 GM13240-14 GM13240-15 GM13240-17 GM13240-18 GM13240-19
## 4 4 4 4 4 4
## GM13240-2 GM13240-21 GM13240-23 GM13240-24 GM13240-25 GM13240-26
## 4 4 4 4 4 4
## GM13240-27 GM13240-28 GM13240-29 GM13240-3 GM13240-30 GM13240-32
## 4 4 4 4 4 4
## GM13240-33 GM13240-34 GM13240-38 GM13240-39 GM13240-40 GM13240-41
## 4 4 4 4 4 4
## GM13240-42 GM13240-43 GM13240-44 GM13240-46 GM13240-47 GM13240-48
## 4 4 4 4 4 4
## GM13240-49 GM13240-51 GM13240-52 GM13240-53 GM13240-54 GM13240-55
## 4 4 4 4 4 4
## GM13240-56 GM13240-58 GM13240-6 GM13240-60 GM13240-62 GM13240-63
## 4 4 4 4 4 4
## GM13240-64 GM13240-65 GM13240-8 COL1505 COL2246 CR138
## 4 4 4 8 8 8
## SM3759-36 VEN208 PER183 COL1722 VEN25 COL1910
## 8 8 38 44 44 46The checks consist of seven fullsib parents and the individuals CR138 and PER183.
##
## 1
## 68data.pheno$check[which(substr(data.pheno$accession_name, 1, 2) == "GM")] <- 0
data.pheno$check[which(substr(data.pheno$accession_name, 1, 2) != "GM")] <- 1
table(data.pheno$check)##
## 0 1
## 1256 212##
## 0 1
## 212 1256## plot_name trial_harvest plot_number rep_number
## 1 202002CQQU1_ciat_rep1_GM13159-1_1 D1_1st 1 1
## 2 202002CQQU1_ciat_rep1_GM13159-4_2 D1_1st 2 1
## 3 202002CQQU1_ciat_rep1_GM13159-6_3 D1_1st 3 1
## 4 202002CQQU1_ciat_rep1_GM13159-7_4 D1_1st 4 1
## 5 202002CQQU1_ciat_rep1_GM13159-8_5 D1_1st 5 1
## 6 202002CQQU1_ciat_rep1_GM13159-9_6 D1_1st 6 1
## accession_name col_number row_number check pop variable value
## 1 GM13159-1 1 1 0 1 number_germinated 6
## 2 GM13159-4 1 2 0 1 number_germinated 5
## 3 GM13159-6 1 3 0 1 number_germinated 6
## 4 GM13159-7 1 4 0 1 number_germinated 4
## 5 GM13159-8 1 5 0 1 number_germinated 6
## 6 GM13159-9 1 6 0 1 number_germinated 5ggplot(data = long.data, aes(x = trial_harvest, y = value)) + geom_boxplot() +
geom_point(data = long.data %>% filter(check == 1), aes(color = accession_name),
position = "jitter", alpha = 0.5) +
facet_wrap(~ variable, scales = "free_y", nrow = 3) + theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1), legend.position = "bottom")## Warning: Removed 12204 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 1798 rows containing missing values (`geom_point()`).
traits <- traits[-c(1:9)]
long.data <- long.data %>% filter(variable %in% traits)
long.data$variable <- factor(long.data$variable, levels = traits)
head(long.data)## plot_name trial_harvest plot_number rep_number
## 1 202002CQQU1_ciat_rep1_GM13159-1_1 D1_1st 1 1
## 2 202002CQQU1_ciat_rep1_GM13159-4_2 D1_1st 2 1
## 3 202002CQQU1_ciat_rep1_GM13159-6_3 D1_1st 3 1
## 4 202002CQQU1_ciat_rep1_GM13159-7_4 D1_1st 4 1
## 5 202002CQQU1_ciat_rep1_GM13159-8_5 D1_1st 5 1
## 6 202002CQQU1_ciat_rep1_GM13159-9_6 D1_1st 6 1
## accession_name col_number row_number check pop variable value
## 1 GM13159-1 1 1 0 1 DM_NIRS 40.97823
## 2 GM13159-4 1 2 0 1 DM_NIRS 41.77826
## 3 GM13159-6 1 3 0 1 DM_NIRS 45.29284
## 4 GM13159-7 1 4 0 1 DM_NIRS 43.35637
## 5 GM13159-8 1 5 0 1 DM_NIRS 43.76374
## 6 GM13159-9 1 6 0 1 DM_NIRS 43.24713ggplot(data = long.data, aes(x = trial_harvest, y = value)) +
geom_boxplot() + geom_point(data = long.data %>% filter(check == 1), aes(color = accession_name),
position = "jitter", alpha = 0.5) +
facet_wrap(~ variable, scales = "free_y", nrow = 2) + theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1), legend.position = "bottom")## Warning: Removed 11042 rows containing non-finite values (`stat_boxplot()`).## Warning: Removed 1654 rows containing missing values (`geom_point()`).
Mixed Model Analysis
For the selected traits (DM_NIRS, WAB_20min_mean, and WAB_30min_mean), the mixed model below will be fitted to estimate: (i) variance components, (ii) broad-sense heritability (plant level), and (iii) predictions. Genetic correlations will be also estimated using the predictions. Additionally, the genetic effects will be declared as a fixed term in the model, and the BLUEs of individuals will actually be explored for genetic mapping. The aim is to avoid double shrinkage due to the inclusion of random genetic effects specifically in the GWAS model.
\[y_{ij} = \mu + t_j + g_i + f(r,c) + \varepsilon_{ij}\] where \(y_{ij}\) is the phenotypic observation of genotype \(i\) in trial \(j\), \(t_j\) is the fixed effect of trial \(j\) (\(j=1,\dots,J\); \(J=4\)), \(g_i\) is the effect of genotype \(i\) separated into random effects of progenies (\(i=1,\dots,I_g\); \(I_g=381\), with \(g_i \sim N(0, \sigma^2_g)\), and fixed effects of parental checks (\(i=I_g+1,\dots,I_g+I_c\); \(I_c=9\), \(f(r,c)\) is the random effect of irregular trends considering the row and column levels, and \(\varepsilon_{ij}\) is the residual random error with \(\varepsilon_{ij} \sim N(0, \sigma^2)\).
DM_NIRS
model.DM <- mmer(DM_NIRS ~ trial_harvest + check:accession_name,
random = ~ pop:accession_name + spl2Da(row_number, col_number, at.var = trial_harvest),
rcov = ~ vsr(dsr(trial_harvest), units), dateWarning = FALSE, date.warning = FALSE,
data = data.pheno, verbose = TRUE)## Warning: fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients## iteration LogLik wall cpu(sec) restrained
## 1 -176.996 15:58:47 14 0
## 2 -41.3007 15:59:2 29 0
## 3 -29.2857 15:59:16 43 0
## 4 -28.1131 15:59:30 57 0
## 5 -28.0385 15:59:44 71 0
## 6 -28.0296 15:59:58 85 0
## 7 -28.0284 16:0:12 99 0
## 8 -28.0282 16:0:27 114 0## ====================================================================
## Multivariate Linear Mixed Model fit by REML
## ************************** sommer 4.3 **************************
## ====================================================================
## logLik AIC BIC Method Converge
## Value -28.0282 82.0564 146.9417 NR TRUE
## ====================================================================
## Variance-Covariance components:
## VarComp VarCompSE Zratio Constraint
## pop:accession_name.DM_NIRS-DM_NIRS 15.131 1.5449 9.794 Positive
## D1_1st:all.DM_NIRS-DM_NIRS 32.358 9.8910 3.271 Positive
## D1_2nd:all.DM_NIRS-DM_NIRS 46.956 14.3520 3.272 Positive
## P1_1st:all.DM_NIRS-DM_NIRS 14.691 4.6024 3.192 Positive
## P1_2nd:all.DM_NIRS-DM_NIRS 16.321 6.0774 2.685 Positive
## D1_1st:units.DM_NIRS-DM_NIRS 11.750 1.5606 7.529 Positive
## D1_2nd:units.DM_NIRS-DM_NIRS 17.334 2.2024 7.871 Positive
## P1_1st:units.DM_NIRS-DM_NIRS 6.721 0.9278 7.244 Positive
## P1_2nd:units.DM_NIRS-DM_NIRS 7.210 1.1300 6.380 Positive
## ====================================================================
## Fixed effects:
## Trait Effect Estimate Std.Error t.value
## 1 DM_NIRS (Intercept) 40.242 0.4162 96.694
## 2 DM_NIRS trial_harvestD1_2nd -2.187 0.5367 -4.075
## 3 DM_NIRS trial_harvestP1_1st 5.567 0.4081 13.640
## 4 DM_NIRS trial_harvestP1_2nd 1.906 0.4386 4.345
## 5 DM_NIRS check:accession_nameCOL1505 1.447 1.3013 1.112
## 6 DM_NIRS check:accession_nameCOL1722 6.103 0.6226 9.803
## 7 DM_NIRS check:accession_nameCOL1910 -11.095 0.6402 -17.331
## 8 DM_NIRS check:accession_nameCOL2246 -5.027 1.2687 -3.962
## ... please access the object to see more
## ====================================================================
## Groups and observations:
## DM_NIRS
## pop:accession_name 390
## D1_1st:all 168
## D1_2nd:all 168
## P1_1st:all 168
## P1_2nd:all 168
## ====================================================================
## Use the '$' sign to access results and parameters## Trait Effect Estimate
## 1 DM_NIRS (Intercept) 40.241681
## 2 DM_NIRS trial_harvestD1_2nd -2.187065
## 3 DM_NIRS trial_harvestP1_1st 5.566800
## 4 DM_NIRS trial_harvestP1_2nd 1.905547
## 5 DM_NIRS check:accession_nameCOL1505 1.447390
## 6 DM_NIRS check:accession_nameCOL1722 6.102948
## 7 DM_NIRS check:accession_nameCOL1910 -11.094830
## 8 DM_NIRS check:accession_nameCOL2246 -5.026892
## 9 DM_NIRS check:accession_nameCR138 -4.030616
## 10 DM_NIRS check:accession_namePER183 -3.676844
## 11 DM_NIRS check:accession_nameSM3759-36 -3.171123
## 12 DM_NIRS check:accession_nameVEN208 3.018480
## 13 DM_NIRS check:accession_nameVEN25 4.809622## VarComp VarCompSE Zratio Constraint
## pop:accession_name.DM_NIRS-DM_NIRS 15.130533 1.5448694 9.794053 Positive
## D1_1st:all.DM_NIRS-DM_NIRS 32.358140 9.8910195 3.271467 Positive
## D1_2nd:all.DM_NIRS-DM_NIRS 46.956028 14.3520160 3.271737 Positive
## P1_1st:all.DM_NIRS-DM_NIRS 14.690987 4.6024254 3.192010 Positive
## P1_2nd:all.DM_NIRS-DM_NIRS 16.320737 6.0774459 2.685460 Positive
## D1_1st:units.DM_NIRS-DM_NIRS 11.750305 1.5605936 7.529382 Positive
## D1_2nd:units.DM_NIRS-DM_NIRS 17.334311 2.2023727 7.870744 Positive
## P1_1st:units.DM_NIRS-DM_NIRS 6.721113 0.9278185 7.243995 Positive
## P1_2nd:units.DM_NIRS-DM_NIRS 7.209525 1.1300192 6.380002 Positive## Version out of date. Please update sommer to the newest version using:
## install.packages('sommer') in a new session
## Use the 'dateWarning' argument to disable the warning message.Version out of date. Please update sommer to the newest version using:
## install.packages('sommer') in a new session
## Use the 'dateWarning' argument to disable the warning message.Version out of date. Please update sommer to the newest version using:
## install.packages('sommer') in a new session
## Use the 'dateWarning' argument to disable the warning message.
## pop:accession_nameCOL1505 pop:accession_nameCOL1722
## 40.24168 40.24168
## pop:accession_nameCOL1910 pop:accession_nameCOL2246
## 40.24168 40.24168
## pop:accession_nameCR138 pop:accession_nameGM13159-1
## 40.24168 35.89062
## pop:accession_nameGM13159-10 pop:accession_nameGM13159-11
## 33.67843 37.55593
## pop:accession_nameGM13159-12 pop:accession_nameGM13159-13
## 34.66427 37.01552## [1] 390## Estimate SE
## h2 0.5845437 0.03113292WAB_20min_mean
model.WAB20 <- mmer(WAB_20min_mean ~ trial_harvest + check:accession_name,
random = ~ pop:accession_name + spl2Da(row_number, col_number, at.var = trial_harvest),
rcov = ~ vsr(dsr(trial_harvest), units), dateWarning = FALSE, date.warning = FALSE,
data = data.pheno, verbose = TRUE)## Warning: fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients## iteration LogLik wall cpu(sec) restrained
## 1 -307.56 16:0:57 7 0
## 2 -237.841 16:1:5 15 0
## 3 -224.149 16:1:12 22 0
## 4 -222.496 16:1:19 29 0
## 5 -222.352 16:1:26 36 0
## 6 -222.338 16:1:34 44 0
## 7 -222.337 16:1:41 51 0
## 8 -222.337 16:1:48 58 0## ==================================================================================
## Multivariate Linear Mixed Model fit by REML
## ********************************* sommer 4.3 *********************************
## ==================================================================================
## logLik AIC BIC Method Converge
## Value -222.3366 470.6731 532.738 NR TRUE
## ==================================================================================
## Variance-Covariance components:
## VarComp VarCompSE Zratio
## pop:accession_name.WAB_20min_mean-WAB_20min_mean 1.1262 0.13950 8.074
## D1_1st:all.WAB_20min_mean-WAB_20min_mean 3.1397 1.22137 2.571
## D1_2nd:all.WAB_20min_mean-WAB_20min_mean 5.5928 2.01235 2.779
## P1_1st:all.WAB_20min_mean-WAB_20min_mean 0.6414 0.33938 1.890
## P1_2nd:all.WAB_20min_mean-WAB_20min_mean 1.2842 0.73352 1.751
## D1_1st:units.WAB_20min_mean-WAB_20min_mean 2.0035 0.25949 7.721
## D1_2nd:units.WAB_20min_mean-WAB_20min_mean 2.2295 0.34065 6.545
## P1_1st:units.WAB_20min_mean-WAB_20min_mean 0.3999 0.09866 4.054
## P1_2nd:units.WAB_20min_mean-WAB_20min_mean 1.5814 0.20888 7.571
## Constraint
## pop:accession_name.WAB_20min_mean-WAB_20min_mean Positive
## D1_1st:all.WAB_20min_mean-WAB_20min_mean Positive
## D1_2nd:all.WAB_20min_mean-WAB_20min_mean Positive
## P1_1st:all.WAB_20min_mean-WAB_20min_mean Positive
## P1_2nd:all.WAB_20min_mean-WAB_20min_mean Positive
## D1_1st:units.WAB_20min_mean-WAB_20min_mean Positive
## D1_2nd:units.WAB_20min_mean-WAB_20min_mean Positive
## P1_1st:units.WAB_20min_mean-WAB_20min_mean Positive
## P1_2nd:units.WAB_20min_mean-WAB_20min_mean Positive
## ==================================================================================
## Fixed effects:
## Trait Effect Estimate Std.Error t.value
## 1 WAB_20min_mean (Intercept) 2.97681 0.1480 20.1168
## 2 WAB_20min_mean trial_harvestD1_2nd -0.30346 0.2136 -1.4206
## 3 WAB_20min_mean trial_harvestP1_1st -0.72974 0.1469 -4.9677
## 4 WAB_20min_mean trial_harvestP1_2nd -0.66537 0.1695 -3.9249
## 5 WAB_20min_mean check:accession_nameCOL1505 1.09802 0.5152 2.1312
## 6 WAB_20min_mean check:accession_nameCOL1722 2.62248 0.4030 6.5078
## 7 WAB_20min_mean check:accession_nameCOL1910 -2.11103 0.4188 -5.0405
## 8 WAB_20min_mean check:accession_nameCOL2246 -0.06319 0.4888 -0.1293
## ... please access the object to see more
## ==================================================================================
## Groups and observations:
## WAB_20min_mean
## pop:accession_name 390
## D1_1st:all 168
## D1_2nd:all 168
## P1_1st:all 168
## P1_2nd:all 168
## ==================================================================================
## Use the '$' sign to access results and parameters## Trait Effect Estimate
## 1 WAB_20min_mean (Intercept) 2.97681434
## 2 WAB_20min_mean trial_harvestD1_2nd -0.30346064
## 3 WAB_20min_mean trial_harvestP1_1st -0.72974129
## 4 WAB_20min_mean trial_harvestP1_2nd -0.66537459
## 5 WAB_20min_mean check:accession_nameCOL1505 1.09802428
## 6 WAB_20min_mean check:accession_nameCOL1722 2.62247758
## 7 WAB_20min_mean check:accession_nameCOL1910 -2.11103481
## 8 WAB_20min_mean check:accession_nameCOL2246 -0.06318821
## 9 WAB_20min_mean check:accession_nameCR138 -0.79405727
## 10 WAB_20min_mean check:accession_namePER183 1.09423461
## 11 WAB_20min_mean check:accession_nameSM3759-36 7.57136670
## 12 WAB_20min_mean check:accession_nameVEN208 1.93686713
## 13 WAB_20min_mean check:accession_nameVEN25 -0.95536879## VarComp VarCompSE Zratio
## pop:accession_name.WAB_20min_mean-WAB_20min_mean 1.1262473 0.13949616 8.073680
## D1_1st:all.WAB_20min_mean-WAB_20min_mean 3.1397479 1.22137294 2.570671
## D1_2nd:all.WAB_20min_mean-WAB_20min_mean 5.5927599 2.01234638 2.779223
## P1_1st:all.WAB_20min_mean-WAB_20min_mean 0.6413824 0.33938442 1.889840
## P1_2nd:all.WAB_20min_mean-WAB_20min_mean 1.2841542 0.73352499 1.750662
## D1_1st:units.WAB_20min_mean-WAB_20min_mean 2.0034839 0.25949231 7.720783
## D1_2nd:units.WAB_20min_mean-WAB_20min_mean 2.2294915 0.34064789 6.544856
## P1_1st:units.WAB_20min_mean-WAB_20min_mean 0.3999497 0.09866029 4.053806
## P1_2nd:units.WAB_20min_mean-WAB_20min_mean 1.5813521 0.20887573 7.570779
## Constraint
## pop:accession_name.WAB_20min_mean-WAB_20min_mean Positive
## D1_1st:all.WAB_20min_mean-WAB_20min_mean Positive
## D1_2nd:all.WAB_20min_mean-WAB_20min_mean Positive
## P1_1st:all.WAB_20min_mean-WAB_20min_mean Positive
## P1_2nd:all.WAB_20min_mean-WAB_20min_mean Positive
## D1_1st:units.WAB_20min_mean-WAB_20min_mean Positive
## D1_2nd:units.WAB_20min_mean-WAB_20min_mean Positive
## P1_1st:units.WAB_20min_mean-WAB_20min_mean Positive
## P1_2nd:units.WAB_20min_mean-WAB_20min_mean Positive## Version out of date. Please update sommer to the newest version using:
## install.packages('sommer') in a new session
## Use the 'dateWarning' argument to disable the warning message.Version out of date. Please update sommer to the newest version using:
## install.packages('sommer') in a new session
## Use the 'dateWarning' argument to disable the warning message.Version out of date. Please update sommer to the newest version using:
## install.packages('sommer') in a new session
## Use the 'dateWarning' argument to disable the warning message.
BLUPs.WAB20 <- coef.mmer(model.WAB20)$Estimate[1] + randef(model.WAB20)[[1]]$WAB_20min_mean
head(BLUPs.WAB20, 10)## pop:accession_nameCOL1505 pop:accession_nameCOL1722
## 2.976814 2.976814
## pop:accession_nameCOL1910 pop:accession_nameCOL2246
## 2.976814 2.976814
## pop:accession_nameCR138 pop:accession_nameGM13159-1
## 2.976814 1.576216
## pop:accession_nameGM13159-10 pop:accession_nameGM13159-11
## 2.134595 5.993458
## pop:accession_nameGM13159-12 pop:accession_nameGM13159-13
## 1.607380 2.875990## [1] 390## Estimate SE
## h2 0.4202703 0.03725122WAB_30min_mean
model.WAB30 <- mmer(WAB_30min_mean ~ trial_harvest + check:accession_name,
random = ~ pop:accession_name + spl2Da(row_number, col_number, at.var = trial_harvest),
rcov = ~ vsr(dsr(trial_harvest), units), dateWarning = FALSE, date.warning = FALSE,
data = data.pheno, verbose = TRUE)## Warning: fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients## iteration LogLik wall cpu(sec) restrained
## 1 -295.345 16:2:8 8 0
## 2 -251.068 16:2:15 15 0
## 3 -238.472 16:2:23 23 0
## 4 -235.377 16:2:30 30 0
## 5 -234.965 16:2:37 37 0
## 6 -234.909 16:2:45 45 0
## 7 -234.902 16:2:52 52 0
## 8 -234.9 16:2:59 59 0
## 9 -234.9 16:3:6 66 0## ==================================================================================
## Multivariate Linear Mixed Model fit by REML
## ********************************* sommer 4.3 *********************************
## ==================================================================================
## logLik AIC BIC Method Converge
## Value -234.9002 495.8004 557.8653 NR TRUE
## ==================================================================================
## Variance-Covariance components:
## VarComp VarCompSE Zratio
## pop:accession_name.WAB_30min_mean-WAB_30min_mean 6.691 0.9270 7.218
## D1_1st:all.WAB_30min_mean-WAB_30min_mean 23.001 9.7289 2.364
## D1_2nd:all.WAB_30min_mean-WAB_30min_mean 43.803 14.6018 3.000
## P1_1st:all.WAB_30min_mean-WAB_30min_mean 6.037 2.9364 2.056
## P1_2nd:all.WAB_30min_mean-WAB_30min_mean 9.290 4.5056 2.062
## D1_1st:units.WAB_30min_mean-WAB_30min_mean 18.224 2.2608 8.061
## D1_2nd:units.WAB_30min_mean-WAB_30min_mean 14.224 2.2221 6.401
## P1_1st:units.WAB_30min_mean-WAB_30min_mean 4.277 0.8263 5.176
## P1_2nd:units.WAB_30min_mean-WAB_30min_mean 7.795 1.1362 6.860
## Constraint
## pop:accession_name.WAB_30min_mean-WAB_30min_mean Positive
## D1_1st:all.WAB_30min_mean-WAB_30min_mean Positive
## D1_2nd:all.WAB_30min_mean-WAB_30min_mean Positive
## P1_1st:all.WAB_30min_mean-WAB_30min_mean Positive
## P1_2nd:all.WAB_30min_mean-WAB_30min_mean Positive
## D1_1st:units.WAB_30min_mean-WAB_30min_mean Positive
## D1_2nd:units.WAB_30min_mean-WAB_30min_mean Positive
## P1_1st:units.WAB_30min_mean-WAB_30min_mean Positive
## P1_2nd:units.WAB_30min_mean-WAB_30min_mean Positive
## ==================================================================================
## Fixed effects:
## Trait Effect Estimate Std.Error t.value
## 1 WAB_30min_mean (Intercept) 7.2328 0.4160 17.386
## 2 WAB_30min_mean trial_harvestD1_2nd -0.8794 0.5859 -1.501
## 3 WAB_30min_mean trial_harvestP1_1st -2.6873 0.4270 -6.293
## 4 WAB_30min_mean trial_harvestP1_2nd -2.7938 0.4567 -6.117
## 5 WAB_30min_mean check:accession_nameCOL1505 5.3769 1.4351 3.747
## 6 WAB_30min_mean check:accession_nameCOL1722 4.3541 1.0047 4.333
## 7 WAB_30min_mean check:accession_nameCOL1910 -4.4002 1.0788 -4.079
## 8 WAB_30min_mean check:accession_nameCOL2246 0.2657 1.3623 0.195
## ... please access the object to see more
## ==================================================================================
## Groups and observations:
## WAB_30min_mean
## pop:accession_name 390
## D1_1st:all 168
## D1_2nd:all 168
## P1_1st:all 168
## P1_2nd:all 168
## ==================================================================================
## Use the '$' sign to access results and parameters## Trait Effect Estimate
## 1 WAB_30min_mean (Intercept) 7.2328264
## 2 WAB_30min_mean trial_harvestD1_2nd -0.8793716
## 3 WAB_30min_mean trial_harvestP1_1st -2.6872744
## 4 WAB_30min_mean trial_harvestP1_2nd -2.7937701
## 5 WAB_30min_mean check:accession_nameCOL1505 5.3768630
## 6 WAB_30min_mean check:accession_nameCOL1722 4.3540543
## 7 WAB_30min_mean check:accession_nameCOL1910 -4.4001725
## 8 WAB_30min_mean check:accession_nameCOL2246 0.2656987
## 9 WAB_30min_mean check:accession_nameCR138 -0.7716785
## 10 WAB_30min_mean check:accession_namePER183 6.1056026
## 11 WAB_30min_mean check:accession_nameSM3759-36 19.4551315
## 12 WAB_30min_mean check:accession_nameVEN208 7.2631540
## 13 WAB_30min_mean check:accession_nameVEN25 -2.9688210## VarComp VarCompSE Zratio
## pop:accession_name.WAB_30min_mean-WAB_30min_mean 6.690933 0.9270149 7.217719
## D1_1st:all.WAB_30min_mean-WAB_30min_mean 23.001436 9.7288644 2.364247
## D1_2nd:all.WAB_30min_mean-WAB_30min_mean 43.803255 14.6018301 2.999847
## P1_1st:all.WAB_30min_mean-WAB_30min_mean 6.037442 2.9363531 2.056102
## P1_2nd:all.WAB_30min_mean-WAB_30min_mean 9.290160 4.5056479 2.061892
## D1_1st:units.WAB_30min_mean-WAB_30min_mean 18.223919 2.2607587 8.060975
## D1_2nd:units.WAB_30min_mean-WAB_30min_mean 14.223766 2.2221374 6.400939
## P1_1st:units.WAB_30min_mean-WAB_30min_mean 4.276745 0.8262561 5.176053
## P1_2nd:units.WAB_30min_mean-WAB_30min_mean 7.795066 1.1362281 6.860477
## Constraint
## pop:accession_name.WAB_30min_mean-WAB_30min_mean Positive
## D1_1st:all.WAB_30min_mean-WAB_30min_mean Positive
## D1_2nd:all.WAB_30min_mean-WAB_30min_mean Positive
## P1_1st:all.WAB_30min_mean-WAB_30min_mean Positive
## P1_2nd:all.WAB_30min_mean-WAB_30min_mean Positive
## D1_1st:units.WAB_30min_mean-WAB_30min_mean Positive
## D1_2nd:units.WAB_30min_mean-WAB_30min_mean Positive
## P1_1st:units.WAB_30min_mean-WAB_30min_mean Positive
## P1_2nd:units.WAB_30min_mean-WAB_30min_mean Positive## Version out of date. Please update sommer to the newest version using:
## install.packages('sommer') in a new session
## Use the 'dateWarning' argument to disable the warning message.Version out of date. Please update sommer to the newest version using:
## install.packages('sommer') in a new session
## Use the 'dateWarning' argument to disable the warning message.Version out of date. Please update sommer to the newest version using:
## install.packages('sommer') in a new session
## Use the 'dateWarning' argument to disable the warning message.
BLUPs.WAB30 <- coef.mmer(model.WAB30)$Estimate[1] + randef(model.WAB30)[[1]]$WAB_30min_mean
head(BLUPs.WAB30, 10)## pop:accession_nameCOL1505 pop:accession_nameCOL1722
## 7.232826 7.232826
## pop:accession_nameCOL1910 pop:accession_nameCOL2246
## 7.232826 7.232826
## pop:accession_nameCR138 pop:accession_nameGM13159-1
## 7.232826 4.915531
## pop:accession_nameGM13159-10 pop:accession_nameGM13159-11
## 6.323899 14.641080
## pop:accession_nameGM13159-12 pop:accession_nameGM13159-13
## 4.951580 7.431961## [1] 390## Estimate SE
## h2 0.3754562 0.03956997heritability <- rbind(heritability.DM, heritability.WAB20, heritability.WAB30)
rownames(heritability) <- c("DM_NIRS", "WAB_20min_mean", "WAB_30min_mean")
knitr::kable(heritability)| Estimate | SE | |
|---|---|---|
| DM_NIRS | 0.5845437 | 0.0311329 |
| WAB_20min_mean | 0.4202703 | 0.0372512 |
| WAB_30min_mean | 0.3754562 | 0.0395700 |
## BLUPs.DM BLUPs.WAB20 BLUPs.WAB30
## pop:accession_nameCOL1505 40.24168 2.976814 7.232826
## pop:accession_nameCOL1722 40.24168 2.976814 7.232826
## pop:accession_nameCOL1910 40.24168 2.976814 7.232826
## pop:accession_nameCOL2246 40.24168 2.976814 7.232826
## pop:accession_nameCR138 40.24168 2.976814 7.232826
## pop:accession_nameGM13159-1 35.89062 1.576216 4.915531
## pop:accession_nameGM13159-10 33.67843 2.134595 6.323899
## pop:accession_nameGM13159-11 37.55593 5.993458 14.641080
## pop:accession_nameGM13159-12 34.66427 1.607380 4.951580
## pop:accession_nameGM13159-13 37.01552 2.875990 7.431961colnames(BLUPs.DM.WAB20.WAB30) <- c("DM_NIRS", "WAB_20min_mean", "WAB_30min_mean")
correlation <- cor(BLUPs.DM.WAB20.WAB30)
correlation## DM_NIRS WAB_20min_mean WAB_30min_mean
## DM_NIRS 1.0000000 0.5336826 0.3769754
## WAB_20min_mean 0.5336826 1.0000000 0.9144480
## WAB_30min_mean 0.3769754 0.9144480 1.0000000
Genetic Linkage Map
As it was said in the introductory section, we will build a genetic linkage map for one of the five fullsib families, which constitute the first population of Cassava. Let’s load the VCF file containing the sample of 9,000 markers and the individuals of both populations:
## Scanning file to determine attributes.
## File attributes:
## meta lines: 114
## header_line: 115
## variant count: 9000
## column count: 618
##
Meta line 114 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
## Character matrix gt rows: 9000
## Character matrix gt cols: 618
## skip: 0
## nrows: 9000
## row_num: 0
##
Processed variant 1000
Processed variant 2000
Processed variant 3000
Processed variant 4000
Processed variant 5000
Processed variant 6000
Processed variant 7000
Processed variant 8000
Processed variant 9000
Processed variant: 9000
## All variants processed## [1] "***** Object of class 'vcfR' *****"
## [1] "***** Meta section *****"
## [1] "##fileformat=VCFv4.2"
## [1] "##FILTER=<ID=snp_filter,Description=\"QD < 2.0 || FS > 60.0 || MQ < 4 [Truncated]"
## [1] "##FORMAT=<ID=AD,Number=R,Type=Integer,Description=\"Allelic depths fo [Truncated]"
## [1] "##FORMAT=<ID=DP,Number=1,Type=Integer,Description=\"Read depth\">"
## [1] "##FORMAT=<ID=GQ,Number=1,Type=Integer,Description=\"Genotype quality\">"
## [1] "##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">"
## [1] "First 6 rows."
## [1]
## [1] "***** Fixed section *****"
## CHROM POS ID REF ALT QUAL FILTER
## [1,] "chr09" "36007461" NA "A" "G" "35529.40" "PASS"
## [2,] "chr10" "20626079" NA "A" "T" "221719" "PASS"
## [3,] "chr12" "28750390" NA "A" "C" "24385.70" "PASS"
## [4,] "chr17" "1253873" NA "G" "A" "30410" "PASS"
## [5,] "chr15" "25005084" NA "G" "A" "61007.20" "PASS"
## [6,] "chr01" "22541997" NA "C" "T" "77006.90" "PASS"
## [1]
## [1] "***** Genotype section *****"
## FORMAT 202023_100_PER183
## [1,] "GT:AD:DP:GQ:PGT:PID:PL" "0/0:13,0:13:36:.:.:0,36,540"
## [2,] "GT:AD:DP:GQ:PGT:PID:PL" "1/1:0,43:43:99:.:.:1777,132,0"
## [3,] "GT:AD:DP:GQ:PGT:PID:PL" "0/0:24,0:24:63:.:.:0,63,945"
## [4,] "GT:AD:DP:GQ:PGT:PID:PL" "0/1:11,8:19:99:.:.:143,0,201"
## [5,] "GT:AD:DP:GQ:PGT:PID:PL" "0/0:14,0:14:39:.:.:0,39,585"
## [6,] "GT:AD:DP:GQ:PGT:PID:PL" "0/0:15,0:15:36:.:.:0,36,540"
## 202023_101_GM13174-48
## [1,] "1/1:0,7:7:21:1|1:36007449_C_G:315,21,0"
## [2,] "0/1:8,23:31:99:.:.:459,0,102"
## [3,] "0/0:33,0:33:90:.:.:0,90,1350"
## [4,] "0/0:16,0:16:39:.:.:0,39,585"
## [5,] "0/0:5,0:5:15:.:.:0,15,111"
## [6,] "0/0:21,0:21:57:.:.:0,57,855"
## 202023_102_GM13174-49
## [1,] "1/1:0,2:2:6:1|1:36007449_C_G:90,6,0"
## [2,] "0/1:18,62:80:99:.:.:1297,0,205"
## [3,] "0/0:43,0:43:99:.:.:0,102,1530"
## [4,] "0/0:32,0:32:87:.:.:0,87,1305"
## [5,] "0/0:35,0:35:99:.:.:0,99,1485"
## [6,] "0/1:21,15:36:99:.:.:250,0,367"
## 202023_103_GM13174-50
## [1,] "1/1:0,12:12:39:1|1:36007449_C_G:585,39,0"
## [2,] "0/1:25,59:84:99:.:.:1405,0,349"
## [3,] "0/0:64,0:64:99:.:.:0,120,1800"
## [4,] "0/1:20,13:33:99:.:.:298,0,357"
## [5,] "0/0:21,0:21:60:.:.:0,60,900"
## [6,] "0/0:35,0:35:99:.:.:0,99,1485"
## 202023_104_GM13174-51
## [1,] "1/1:0,5:5:15:1|1:36007449_C_G:225,15,0"
## [2,] "0/1:8,18:26:99:.:.:511,0,105"
## [3,] "0/0:11,0:11:30:.:.:0,30,450"
## [4,] "0/1:11,13:24:99:.:.:253,0,191"
## [5,] "1/1:0,12:12:42:1|1:25005084_G_A:440,42,0"
## [6,] "0/1:13,11:24:99:.:.:184,0,217"
## [1] "First 6 columns only."
## [1]
## [1] "Unique GT formats:"
## [1] "GT:AD:DP:GQ:PGT:PID:PL" "GT:AD:DP:GQ:PL"
## [1]To select one of the fullsib families for linkage map, we need to load the information from population and pedigree of the genotyped individuals:
## Indiv_VCF Indiv_VCF_Edit Population Family
## 1 202023_100_PER183 PER183 P1 PER183
## 2 202023_101_GM13174-48 GM13174-48 P1 COL1910-x-SM3759-36
## 3 202023_102_GM13174-49 GM13174-49 P1 COL1910-x-SM3759-36
## 4 202023_103_GM13174-50 GM13174-50 P1 COL1910-x-SM3759-36
## 5 202023_104_GM13174-51 GM13174-51 P1 COL1910-x-SM3759-36
## 6 202023_105_GM13174-52 GM13174-52 P1 COL1910-x-SM3759-36##
## P1 P2 Parents
## 226 370 13P1 <- which(indiv.vcf$Population == "P1")
Pr <- which(indiv.vcf$Population == "Parents")
indiv.vcf.P1 <- indiv.vcf[c(P1, Pr),]
table(indiv.vcf.P1$Population)##
## P1 Parents
## 226 13Let’s save the individuals from the first population:
corresp.P1 <- colnames(vcf.file.P1.P2@gt) %in% indiv.vcf.P1$Indiv_VCF
columns.to.keep <- c(1,which(corresp.P1 == TRUE))
vcf.file.P1 <- vcf.file.P1.P2 # to get the vcf structure
vcf.file.P1@gt <- vcf.file.P1@gt[,columns.to.keep]
dim(vcf.file.P1@gt) # 227 + 13## [1] 9000 240Checking the amount of individuals for each fullsib family:
number.of.crosses <- rep(NA, times = length(colnames(vcf.file.P1@gt)))
for(i in 2:length(colnames(vcf.file.P1@gt)))
{
name.break <- unlist(strsplit(colnames(vcf.file.P1@gt)[i], split = ""))
if(any(which(name.break == "-")))
{
number.of.crosses[i] <- paste(name.break[(which(name.break == "-")-3):(which(name.break == "-")-1)],
collapse = "")
}
else
number.of.crosses[i] <- paste(name.break[(which(name.break == "_")[2] + 1):(length(name.break))],
collapse = "")
}
table(number.of.crosses)## number.of.crosses
## 159 169 174 175 240 759 COL1722 COL1910 PER183 VEN25
## 49 24 44 44 62 1 5 3 3 4The number 759 above corresponds to the parent SM3759-36. Based on the number of progenies genotyped and the available parents, we will build a genetic linkage map for the fullsib family 159 (SM3759-36 x VEN25). It has 49 individuals and both parents were also genotyped for SNP markers, what was not observed for the other families, except 174 (\(n=44\)). As we need the parents configuration for estimating linkage phases, we will explore the data from family 159, containing the maximum number of individuals.
F159 <- sort(c(which(number.of.crosses == "159"),
which(number.of.crosses == "759"),
which(number.of.crosses == "VEN25")[1])) # first sample of the four available.
length(F159) # 49 individuals + 2 parents.## [1] 51vcf.file.P1.F159 <- vcf.file.P1
vcf.file.P1.F159@gt <- vcf.file.P1.F159@gt[,c(1,F159)]
dim(vcf.file.P1.F159@gt) # 49 individuals + 2 parents + FORMAT## [1] 9000 52Since we have created the VCF file for the family 159 (SM3759-36 x
VEN25), it is possible to read it using the R package
onemap:
data.geno.F159 <- onemap_read_vcfR(vcfR.object = vcf.file.P1.F159, cross = "outcross",
parent1 = "202023_131_SM3759-36", parent2 = "202023_120_VEN25",
only_biallelic = TRUE, verbose = TRUE)## 396 Markers were removed of the dataset because one or both of parents have no informed genotypes (are missing data)
## 5025 Markers were removed from the dataset because both of parents are homozygotes, these markers are considered non-informative in outcrossing populations.Checking the markers and their segregation types:
As showed above, onemap has filtered the fullsib family
data for the informative SNP markers. Thus, we can use their information
of segregation type and coded genotypes to create a new file (extension
.raw), which is needed to perform QTL mapping via
fullsibQTL. This file was created out of this tutorial, and
to accelerate the construction of linkage map, we have selected a subset
of 3,200 SNP markers (out of 3,897). The R codes for this step are not
shown here. But, users can take a look into the .raw file
and check the structure of this.
Since the .raw file was created and saved in the current
directory, it is possible to read it via onemap:
## Working...
##
## --Read the following data:
## Type of cross: outcross
## Number of individuals: 49
## Number of markers: 3200
## Chromosome information: yes
## Position information: yes
## Number of traits: 3
## Missing trait values:
## DM_NIRS: 0
## WAB_20min_mean: 0
## WAB_30min_mean: 0## This is an object of class 'onemap'
## Type of cross: outcross
## No. individuals: 49
## No. markers: 3200
## CHROM information: yes
## POS information: yes
## Percent genotyped: 97
##
## Segregation types:
## B3.7 --> 510
## D1.10 --> 1408
## D2.15 --> 1282
##
## No. traits: 3
## Missing trait values:
## DM_NIRS: 0
## WAB_20min_mean: 0
## WAB_30min_mean: 0Filtering for 25% of missing data:
## Number of markers removed from the onemap object: 129Segregation test:
## Marker H0 Chi-square p-value % genot.
## 1 chr17_1253873 1:1 0.18367347 0.6682351418 100.00
## 2 chr15_25005084 1:1 0.08333333 0.7728299927 97.96
## 3 chr01_22541997 1:1 1.04255319 0.3072284097 95.92
## 4 chr17_10410880 1:1 0.33333333 0.5637028617 97.96
## 5 chr13_36897112 1:1 0.18367347 0.6682351418 100.00
## 6 chr09_6707714 1:1 14.87755102 0.0001147201 100.00
distorted.markers <- select_segreg(segregation, distorted = TRUE, numbers = FALSE, threshold = 0.05)
distorted.markers## [1] "chr06_18632012" "chr18_15923103" "chr10_31642694" "chr13_26775909"
## [5] "chr12_31074973" "chr16_34150212" "chr10_18514269" "chr09_16396224"
## [9] "chr01_10386693" "chr10_31945826" "chr03_21367467" "chr02_35460118"
## [13] "chr18_20884867" "chr04_17474173" "chr06_16865760" "chr04_24895121"
## [17] "chr16_20866712" "chr06_9268572" "chr12_22721070" "chr07_6587253"
## [21] "chr05_23024669" "chr10_22026902" "chr07_4541178" "chr07_923548"
## [25] "chr09_33004893" "chr05_4596643" "chr17_24918424" "chr18_10226728"
## [29] "chr12_8325470" "chr11_13166172" "chr06_18780600" "chr01_25993680"
## [33] "chr16_18284052" "chr01_41246279" "chr18_1964914" "chr18_943005"
## [37] "chr10_13055743" "chr03_31772697" "chr07_4134816" "chr11_5417930"
## [41] "chr12_5410627" "chr09_21731477" "chr12_23622398" "chr13_15293804"
## [45] "chr02_28805611" "chr01_13917360" "chr10_22801185" "chr04_10251505"
## [49] "chr02_19594448" "chr07_17304608" "chr07_13720774" "chr13_27089142"
## [53] "chr18_33462002" "chr01_26679655" "chr15_18722955" "chr10_27053631"
## [57] "chr13_27083420" "chr01_553551" "chr17_15234777" "chr18_2061509"
## [61] "chr10_15961808" "chr01_23693032" "chr13_5636123" "chr18_4335306"
## [65] "chr10_24388626" "chr11_27466234" "chr18_7403503" "chr11_6455957"
## [69] "chr05_17923195" "chr09_28990213" "chr04_8806299" "chr07_13183517"
## [73] "chr13_5635805" "chr13_31238003" "chr18_5005129" "chr09_21599130"
## [77] "chr05_17910779" "chr08_27196526" "chr04_17461424" "chr16_16190873"
## [81] "chr01_23489335" "chr06_27639969" "chr13_28694228" "chr16_20545053"
## [85] "chr15_21380306" "chr11_17244550" "chr10_21814621" "chr17_20137112"
## [89] "chr18_27608523" "chr18_24747009" "chr18_5548624" "chr12_24040358"
## [93] "chr01_8546808" "chr18_3766112" "chr07_15974471" "chr14_22939537"
## [97] "chr11_9884342" "chr08_16340021" "chr01_26839647" "chr01_4481952"
## [101] "chr02_5602975" "chr17_31326992" "chr18_5453654" "chr18_8036799"
## [105] "chr14_17061162" "chr17_9396251" "chr18_10992434" "chr05_24098466"
## [109] "chr10_30216081" "chr07_24238241" "chr06_786036" "chr04_20066383"
## [113] "chr09_11736670" "chr02_35443672" "chr03_16222722" "chr07_30641813"
## [117] "chr17_33811203" "chr06_14389132" "chr16_16012265" "chr12_8256780"
## [121] "chr18_7608225" "chr14_4245899" "chr08_13227181" "chr14_22201809"
## [125] "chr03_15830760" "chr10_2898601" "chr10_21943955" "chr03_11154973"
## [129] "chr04_29063249" "chr16_5285568" "chr02_24603906" "chr06_2062667"
## [133] "chr05_23720383" "chr02_30166081" "chr05_17366604" "chr15_28495335"
## [137] "chr08_23681774" "chr01_20489190" "chr13_24442439" "chr13_8585364"
## [141] "chr06_4760811" "chr04_23526802" "chr13_20326220" "chr09_5219426"
## [145] "chr18_20137112" "chr18_8632685" "chr04_12962635" "chr18_11792492"
## [149] "chr08_27865427" "chr15_26588779" "chr14_17944512" "chr17_17417736"
## [153] "chr18_15219756" "chr01_28758380" "chr07_8407019" "chr17_30384206"
## [157] "chr08_31665276" "chr07_8395937" "chr09_18140250" "chr10_3825090"
## [161] "chr18_8528189" "chr01_17225829" "chr15_27121653" "chr18_12931794"
## [165] "chr09_4836921" "chr13_7524360" "chr13_11062734" "chr05_20662101"
## [169] "chr08_15737797" "chr14_1548251" "chr16_14492601" "chr18_9891817"
## [173] "chr17_20137494" "chr12_25735788" "chr16_13585392" "chr18_7958380"
## [177] "chr02_22891824" "chr02_33116794" "chr14_19193358" "chr17_17293510"
## [181] "chr18_2193834" "chr04_13533082" "chr11_23487006" "chr03_15894213"
## [185] "chr18_8513190" "chr16_16404663" "chr04_18233860" "chr04_25369192"
## [189] "chr10_6354535" "chr16_1430169" "chr18_2164042" "chr14_19597185"
## [193] "chr12_33076986" "chr13_7530763" "chr03_22122940" "chr10_28490306"
## [197] "chr17_22740756" "chr05_17366960" "chr14_18697730" "chr07_26720668"
## [201] "chr18_19072759" "chr02_9949511" "chr18_638347" "chr16_858102"
## [205] "chr12_19615443" "chr12_18226394" "chr18_9099256" "chr01_37312095"
## [209] "chr03_32355634" "chr02_19959035" "chr03_5864214" "chr13_26814245"
## [213] "chr09_28410057" "chr03_15861617" "chr18_19153809" "chr11_7943488"
## [217] "chr18_2104383" "chr09_14910545" "chr07_3237693" "chr07_34361898"
## [221] "chr11_17513190" "chr18_9226812" "chr06_18476256" "chr04_19671995"
## [225] "chr12_16117110" "chr18_8880184" "chr08_9414178" "chr07_4269668"
## [229] "chr10_21177218" "chr18_14745558" "chr09_9835867" "chr03_27787737"
## [233] "chr07_30873408" "chr16_13882147" "chr10_15528328" "chr07_10728493"
## [237] "chr05_10237909" "chr02_17354391" "chr02_32402030" "chr17_29213846"
## [241] "chr13_20551610" "chr07_3252389" "chr18_1295194" "chr07_23856067"
## [245] "chr01_4720163" "chr02_18175476" "chr08_25166908" "chr02_20127151"
## [249] "chr03_18801960"## [1] 249no.distorted.markers <- select_segreg(segregation, distorted = FALSE, numbers = FALSE, threshold = 0.05)
length(no.distorted.markers)## [1] 2822For this moment, we will not remove distorted SNP markers to build the linkage map. Usually, they are initially removed and the map is built only with non-distorted markers. With an acceptable map for each linkage group, the distorted markers are tried to be included into their group (one-by-one), which can extend to be more saturated.
Recombination fraction estimation via two-point approach:
## Computing 4713985 recombination fractions:
##
## 0% .................................................. 15%
## 15% .................................................. 29%
## 29% .................................................. 42%
## 42% .................................................. 53%
## 53% .................................................. 64%
## 64% .................................................. 73%
## 73% .................................................. 81%
## 81% .................................................. 87%
## 87% .................................................. 92%
## 92% .................................................. 96%
## 96% .................................................. 98%
## 98% .................................................. 99%
## 99% ................ 100%To build the linkage map, we will consider the Cassava reference genome. In this sense, the linkage groups and the orders of molecular markers will be defined considering the chromosome and position information, which were already registered in the input file. Therefore, for each of the chromosomes and its order, we will only update the linkage phase and recombination fraction estimates via multipoint approach, which considers the use of hidden Markov models (HMM) to build the final linkage map.
CHR.map.cassava <- list()
for(i in 1:length(unique(data.geno$CHROM)))
{
print(i)
CHR.temp <- make_seq(twopts, sort(unique(data.geno$CHROM))[i])
CHR.map.cassava[[i]] <- map(CHR.temp, tol = 1e-03, verbose = TRUE, rm_unlinked = FALSE, global_error = 0.15)
}
save(CHR.map.cassava, file = "CHR_Map_Cassava.RData")As an example, let’s print the heatmaps of the chromosomes 1, 6, and 17. Usually, they are used as diagnostics to check the map for inconsistencies.
QTL Mapping
For QTL mapping, we are going to use an extension of the traditional composite interval mapping (CIM), proposed by Zeng (1993). This extension was specifically developed for outcrossing populations, following the model and procedures described by Gazaffi et al. (2014).
Initially, we need to calculate the QTL conditional probabilities for each 1 cM along the map.
fsib.F159 <- create_fullsib(data.geno.F159, map.list = CHR.map.cassava,
step = 1, map.function = "kosambi")##
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|======================================================================| 100%## This is an object of class 'fullsib'
##
## The linkage map has 18 groups, with 23729.67 cM and 3071 markers
## No. individuals genotyped: 49
## Group 1 : 1388 cM, 235 markers (B3, D1, D2)
## Group 2 : 422.8 cM, 189 markers (B3, D1, D2)
## Group 3 : 796.9 cM, 131 markers (B3, D1, D2)
## Group 4 : 2124 cM, 165 markers (B3, D1, D2)
## Group 5 : 1003 cM, 133 markers (B3, D1, D2)
## Group 6 : 910.8 cM, 142 markers (B3, D1, D2)
## Group 7 : 3038 cM, 197 markers (B3, D1, D2)
## Group 8 : 1236 cM, 202 markers (B3, D1, D2)
## Group 9 : 922 cM, 180 markers (B3, D1, D2)
## Group 10 : 2065 cM, 138 markers (B3, D1, D2)
## Group 11 : 1125 cM, 178 markers (B3, D1, D2)
## Group 12 : 1425 cM, 210 markers (B3, D1, D2)
## Group 13 : 1116 cM, 155 markers (B3, D1, D2)
## Group 14 : 1571 cM, 166 markers (B3, D1, D2)
## Group 15 : 992.6 cM, 204 markers (B3, D1, D2)
## Group 16 : 1988 cM, 185 markers (B3, D1, D2)
## Group 17 : 488.1 cM, 155 markers (B3, D1, D2)
## Group 18 : 1115 cM, 106 markers (B3, D1, D2)
## And 129 unlinked markers
##
##
## 3 phenotypes are avaliable for QTL mapping
## Multipoint probability for QTL genotype was obtained for each 1 cMThe first step is to select markers which will be included as
cofactors in the QTL model. As it was seen in the theory, they are used
to control putative QTL from other intervals, which would be considered
as ghost QTL. The selection of cofactors is performed using
variable selection methods and criteria for model comparison, and the
number of markers cannot be excessive (\(2\sqrt(n)\)) in the model to avoid its
super-parametrization.
With the inclusion of cofactors in the model, and definition of a
windows size underlying the marker intervals, we can do a genome scan
for every cM. LOD profiles will be obtained. However, a threshold should
be calculated and defined to detect QTL. Using the R package
fullsibQTL, a permutation test could be used.
DM_NIRS:
Cofactor selection:
## Number of Cofactors selected: 1 ... 2 ... 3 ... 4 ... 5 ... done
Genome scan:
## QTL mapping for 18 groups
##
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Permutation test:
cim.perm.1 <- cim_scan(fullsib = cofs_fs, lg = "all", pheno.col = 1, LOD = TRUE, n.perm = 1000,
write.perm = "CIM_Permutations_DM_NIRS.txt")
save(cim.perm.1, file = "CIM_Permutations_DM_NIRS.RData")## Threshold considering 1000 permutations## peak.1 peak.2 peak.3 peak.4 peak.5 peak.6 peak.7 peak.8
## 0.05 17.57999 16.54989 16.03948 15.70451 15.46671 15.26978 15.01794 14.79944
## peak.9 peak.10
## 0.05 14.58311 14.31063plot(cim.1, lty = 1, lwd = 2, incl.mkr = NULL, cex.incl = 0.7,
cex.axis = 0.8, col = "red", ylab = "LOD Score", xlab = "Linkage Group",
main = "CIM - DM_NIRS")
abline(h = summary(cim.perm.1, alpha = 0.05)[1,2], col = "blue")
## Threshold considering 1000 permutationsAs we can see, there is evidence of a putative QTL in the chromosome 1 for DM_NIRS. Let’s check if it was placed in any position between two possible SNP markers or exactly in the position of one SNP.
## lg pos.cM LOD model
## chr01_32613009 1 1343.097 22.416653 0
## chr02_24603906 2 258.510 8.718413 0
## loc590 3 590.000 6.292411 0
## loc813 4 813.000 12.250772 0
## loc72 5 72.000 13.567572 0
## loc121 6 121.000 9.886802 0The putative QTL is located at the position of the SNP
chr01_32613009. We can print its effects:
## chr01_32613009
## LG 1.000000e+00
## pos 1.343097e+03
## -log10(pval) 2.152744e+01
## LOD_Ha 2.241716e+01
## mu 3.813223e+01
## alpha_p 4.338996e+00
## LOD_H1 2.195962e+01
## alpha_q 5.045502e-02
## LOD_H2 9.884248e-03
## delta_pq -3.393702e-03
## LOD_H3 4.288349e-05
## H4_pvalue 3.692293e-18
## H5_pvalue 2.728832e-19
## H6_pvalue 8.945677e-01
## model 0.000000e+00
## attr(,"class")
## [1] "fullsib_char" "matrix"Only the additive effect of the parent p (\(\alpha_{p} = 4.34\)) was statistically
significant based on the LOD score statistics (\(LOD_{H1} = 21.96\)), considering a
significance level of \(5\%\). The
parent p is the genotype SM3759-36, which is
classified as a good parent for cooking quality.
QTL segregation:
## QTL segregation is 1:1## [1] "1:1"Map with QTL:
##
## Printing QTL and its linkage phase between markers across the LG:
##
## Markers Position Parent 1 Parent 2
##
## chr01_64792 0.00 a | | a a | | b
## chr01_156276 0.00 a | | b a | | a
## chr01_407922 0.00 b | | a b | | a
## chr01_456504 0.00 b | | a a | | a
## chr01_522058 0.00 a | | b a | | a
## chr01_553551 0.01 a | | b a | | a
## chr01_604888 0.01 b | | a b | | a
## chr01_779593 0.01 b | | a b | | a
## chr01_1170574 0.01 b | | a b | | a
## chr01_1203737 0.01 b | | a a | | a
## chr01_1205059 0.23 a | | a a | | b
## chr01_1513509 1.02 b | | a b | | a
## chr01_1520463 2.02 a | | b a | | a
## chr01_1633957 2.02 a | | b b | | a
## chr01_1707068 3.07 a | | a b | | a
## chr01_1783281 3.08 a | | b a | | b
## chr01_1830667 3.08 a | | b a | | a
## chr01_1861268 3.08 a | | a a | | b
## chr01_1885535 3.08 a | | b a | | a
## chr01_1909667 3.08 a | | b a | | a
## chr01_2004082 3.08 a | | b a | | b
## chr01_2415250 290.90 a | | b a | | a
## chr01_2432095 369.14 a | | b a | | b
## chr01_2449296 369.53 b | | a a | | a
## chr01_2484534 369.53 b | | a a | | a
## chr01_2592090 369.53 a | | a b | | a
## chr01_2743293 369.53 b | | a a | | a
## chr01_3368491 369.53 a | | a a | | b
## chr01_3495915 372.55 b | | a a | | a
## chr01_3583698 374.54 a | | b a | | a
## chr01_3934285 377.85 a | | b a | | b
## chr01_4092932 377.85 a | | b a | | b
## chr01_4285205 377.85 a | | b a | | b
## chr01_4333551 377.85 b | | a a | | a
## chr01_4351910 377.85 a | | a a | | b
## chr01_4481952 413.92 a | | b a | | b
## chr01_4654718 413.92 a | | b a | | a
## chr01_4720163 413.92 a | | b a | | a
## chr01_4838942 454.03 a | | a a | | b
## chr01_4995141 454.03 b | | a b | | a
## chr01_5231305 454.03 b | | a a | | a
## chr01_5234421 454.03 a | | a b | | a
## chr01_5345802 454.03 b | | a a | | a
## chr01_5546938 458.19 a | | a a | | b
## chr01_5835307 458.19 a | | b a | | a
## chr01_5873105 458.19 b | | a a | | a
## chr01_5884298 458.20 a | | b a | | a
## chr01_6095670 459.12 a | | a b | | a
## chr01_6096837 459.45 a | | b a | | a
## chr01_6246086 459.45 a | | a b | | a
## chr01_6262934 459.45 a | | b b | | a
## chr01_6404967 459.45 a | | b a | | a
## chr01_6554090 459.45 b | | a a | | b
## chr01_6660408 460.02 a | | a a | | b
## chr01_6874616 460.02 a | | b b | | a
## chr01_7282970 461.06 b | | a b | | a
## chr01_7303615 461.06 b | | a b | | a
## chr01_7304750 461.06 a | | b a | | a
## chr01_8216215 461.06 b | | a a | | a
## chr01_8248358 461.06 a | | b a | | b
## chr01_8267306 461.06 b | | a a | | a
## chr01_8296625 461.06 b | | a a | | a
## chr01_8378991 461.06 a | | a b | | a
## chr01_8538266 463.06 b | | a a | | a
## chr01_8546808 463.06 a | | a b | | a
## chr01_8613806 463.06 a | | a b | | a
## chr01_8743395 464.28 a | | b a | | a
## chr01_9137148 464.28 b | | a b | | a
## chr01_9195612 464.32 b | | a a | | a
## chr01_9313131 465.63 b | | a b | | a
## chr01_9650703 465.63 a | | b a | | a
## chr01_9665027 465.63 a | | b a | | a
## chr01_9706671 465.63 a | | b a | | a
## chr01_9707169 465.63 a | | a b | | a
## chr01_9897499 465.63 a | | b a | | a
## chr01_10386693 466.70 b | | a b | | a
## chr01_10394249 466.70 b | | a a | | a
## chr01_10459214 466.70 b | | a b | | a
## chr01_10507110 472.84 b | | a b | | a
## chr01_10864521 472.84 a | | a a | | b
## chr01_10866174 472.84 b | | a a | | a
## chr01_12130342 474.96 a | | a a | | b
## chr01_12149915 474.96 a | | a b | | a
## chr01_12639562 474.99 a | | b a | | a
## chr01_12691348 474.99 a | | a a | | b
## chr01_12898407 474.99 a | | a a | | b
## chr01_13729398 487.33 a | | a b | | a
## chr01_13917360 487.33 b | | a b | | a
## chr01_14073951 490.83 a | | a b | | a
## chr01_14405039 490.83 a | | a b | | a
## chr01_14405646 490.83 a | | a b | | a
## chr01_15295957 490.83 b | | a b | | a
## chr01_15661908 498.54 a | | a b | | a
## chr01_15723481 498.54 a | | a b | | a
## chr01_16030996 498.54 a | | a b | | a
## chr01_16031834 498.54 a | | a b | | a
## chr01_16054748 498.55 a | | a b | | a
## chr01_16826063 498.55 a | | a b | | a
## chr01_17120007 498.55 a | | a b | | a
## chr01_17225829 498.55 b | | a a | | a
## chr01_17457946 498.55 a | | a b | | a
## chr01_17974096 498.55 a | | a a | | b
## chr01_18226286 498.55 a | | a a | | b
## chr01_18227727 498.55 a | | a b | | a
## chr01_19295910 498.55 a | | a a | | b
## chr01_19491634 498.55 a | | a b | | a
## chr01_19492635 498.56 a | | a b | | a
## chr01_19789358 498.56 a | | a a | | b
## chr01_19790314 498.56 a | | a a | | b
## chr01_20050245 498.56 a | | a b | | a
## chr01_20088182 498.56 a | | a a | | b
## chr01_20489190 498.56 a | | b a | | a
## chr01_20509239 498.56 a | | a b | | a
## chr01_20636109 498.56 a | | a a | | b
## chr01_20966565 511.10 a | | a b | | a
## chr01_21691298 520.04 a | | b b | | a
## chr01_21907179 529.14 a | | a b | | a
## chr01_21925058 529.14 b | | a a | | a
## chr01_21945573 529.15 a | | a b | | a
## chr01_22158873 529.15 a | | b a | | a
## chr01_22464053 529.15 a | | b b | | a
## chr01_22541997 529.15 b | | a a | | a
## chr01_23068672 529.15 a | | b b | | a
## chr01_23084855 582.83 a | | b a | | b
## chr01_23489335 610.60 a | | a a | | b
## chr01_23657456 638.24 b | | a a | | a
## chr01_23693032 638.24 b | | a a | | b
## chr01_23873016 638.24 a | | b a | | a
## chr01_24034499 638.24 a | | b a | | a
## chr01_24280724 638.27 b | | a a | | a
## chr01_24316666 638.27 a | | b a | | a
## chr01_24390059 659.19 a | | a b | | a
## chr01_24443464 659.19 a | | a a | | b
## chr01_24826610 660.32 a | | a a | | b
## chr01_24941533 660.32 a | | b a | | a
## chr01_24982151 660.32 b | | a a | | b
## chr01_25014191 660.32 b | | a a | | a
## chr01_25016954 660.32 a | | a a | | b
## chr01_25099192 661.36 b | | a a | | b
## chr01_25229368 661.36 a | | b b | | a
## chr01_25289288 661.36 b | | a a | | b
## chr01_25845667 661.36 a | | a a | | b
## chr01_25880934 661.39 a | | b a | | a
## chr01_25881382 709.97 a | | a b | | a
## chr01_25881530 803.55 a | | a a | | b
## chr01_25993680 1091.38 a | | b a | | a
## chr01_26086515 1091.38 a | | a b | | a
## chr01_26089345 1091.38 a | | a b | | a
## chr01_26158581 1091.38 b | | a a | | b
## chr01_26324075 1094.22 a | | a a | | b
## chr01_26431578 1094.22 a | | a a | | b
## chr01_26434520 1094.22 a | | a a | | b
## chr01_26679655 1094.22 a | | b a | | a
## chr01_26771973 1104.42 a | | a b | | a
## chr01_26839647 1104.43 b | | a b | | a
## chr01_26920613 1104.43 a | | a a | | b
## chr01_26979040 1104.43 a | | a b | | a
## chr01_27138077 1104.43 a | | a b | | a
## chr01_28424913 1104.43 a | | a a | | b
## chr01_28535711 1116.10 b | | a a | | a
## chr01_28758380 1154.06 a | | a b | | a
## chr01_29095637 1223.23 a | | a b | | a
## chr01_29201084 1336.84 a | | a a | | b
## chr01_30136967 1336.85 a | | a b | | a
## chr01_30428165 1336.85 a | | a b | | a
## chr01_30432434 1336.85 a | | a b | | a
## chr01_30573400 1339.11 a | | a b | | a
## chr01_30695647 1339.11 a | | a a | | b
## chr01_31004147 1341.16 a | | a b | | a
## chr01_31196354 1341.16 a | | a b | | a
## chr01_31469641 1341.16 a | | a b | | a
## chr01_31529906 1341.16 a | | a b | | a
## chr01_31544211 1343.09 a | | b b | | a
## chr01_31692036 1343.10 a | | a b | | a
## chr01_32001190 1343.10 a | | b a | | a
## QTL 1343.10 P1 | | P2 Q0 | | Q0
## chr01_32687089 1345.46 a | | b a | | a
## chr01_32694761 1348.71 a | | b a | | b
## chr01_33067627 1348.71 b | | a b | | a
## chr01_33199949 1348.71 a | | a a | | b
## chr01_33222617 1348.71 a | | b a | | a
## chr01_33250754 1348.71 a | | b b | | a
## chr01_33422365 1348.71 a | | a a | | b
## chr01_34569381 1348.72 a | | a b | | a
## chr01_34582819 1349.03 a | | a b | | a
## chr01_34671057 1349.71 b | | a b | | a
## chr01_34963322 1351.60 a | | b a | | a
## chr01_35253006 1351.60 b | | a b | | a
## chr01_35739046 1355.64 b | | a b | | a
## chr01_35803322 1355.99 a | | a a | | b
## chr01_35951952 1355.99 a | | b a | | a
## chr01_36210858 1355.99 b | | a b | | a
## chr01_36212929 1355.99 b | | a b | | a
## chr01_36306300 1355.99 a | | a a | | b
## chr01_36342222 1355.99 a | | b a | | a
## chr01_36604810 1356.00 b | | a a | | a
## chr01_36643619 1356.00 a | | b a | | a
## chr01_36671166 1356.00 a | | b a | | a
## chr01_36946456 1358.03 a | | b a | | a
## chr01_37058184 1358.03 b | | a a | | a
## chr01_37225079 1358.03 b | | a a | | a
## chr01_37312095 1359.51 b | | a a | | a
## chr01_37531103 1366.00 a | | b a | | a
## chr01_37812542 1366.00 a | | a a | | b
## chr01_37823545 1366.01 a | | a a | | b
## chr01_37905733 1366.01 b | | a a | | a
## chr01_37977650 1366.01 b | | a a | | a
## chr01_38049236 1367.36 a | | a a | | b
## chr01_38111908 1367.36 a | | a a | | b
## chr01_38243587 1367.36 a | | a a | | b
## chr01_38912456 1374.53 a | | a a | | b
## chr01_39413610 1378.47 a | | b a | | a
## chr01_39492254 1379.28 a | | a b | | a
## chr01_39492949 1379.29 b | | a b | | a
## chr01_39522038 1379.29 a | | a b | | a
## chr01_39741448 1381.16 b | | a b | | a
## chr01_40280810 1381.16 a | | a a | | b
## chr01_40462213 1385.86 b | | a a | | a
## chr01_40568279 1385.86 b | | a a | | a
## chr01_40810441 1385.86 b | | a b | | a
## chr01_40896801 1385.86 b | | a b | | a
## chr01_41068009 1385.86 b | | a a | | a
## chr01_41128450 1385.86 a | | a b | | a
## chr01_41191690 1385.86 a | | b a | | a
## chr01_41246279 1386.73 a | | b a | | a
## chr01_41355934 1386.73 a | | a b | | a
## chr01_41429103 1386.73 a | | b a | | a
## chr01_41640775 1386.73 a | | a b | | a
## chr01_41808553 1387.52 a | | a b | | a
## chr01_41908221 1388.10 a | | a b | | a
## chr01_41959255 1388.10 a | | a a | | b
## chr01_42055651 1388.10 b | | a a | | a
## chr01_42397090 1388.10 a | | b b | | a
## chr01_42524854 1388.10 b | | a a | | b
## chr01_42982896 1388.10 a | | a b | | a
##
## P1 and Q1 have positive effect (increase phenotypic value)
## P2 and Q2 have negative effect (reduce phenotypic value)
## P0 and Q0 have neutral effect (non signif.)Proportion of variation explained by the QTL:
## lg loc r2
## R2.trait NA All 62.6375
## R2.lg1.chr01_32613009 1 chr01_32613009 62.6375The putative QTL located at the position of SNP
chr01_32613009 explains \(62.64\%\) of DM_NIRS variation in the
background of fullsib family 159 (SM3759-36 x VEN25).
WAB_20min_mean:
Cofactor selection:
## Number of Cofactors selected: 1 ... 2 ... 3 ... 4 ... 5 ... done
Genome scan:
## QTL mapping for 18 groups
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Permutation test:
cim.perm.2 <- cim_scan(fullsib = cofs.fs.2, lg = "all", pheno.col = 2, LOD = TRUE, n.perm = 1000,
write.perm = "CIM_Permutations_WAB_20min_mean.txt")
save(cim.perm.2, file = "CIM_Permutations_WAB_20min_mean.RData")## Threshold considering 1000 permutations## peak.1 peak.2 peak.3 peak.4 peak.5 peak.6 peak.7 peak.8
## 0.05 17.70167 16.45719 15.8817 15.57247 15.28726 14.97341 14.73333 14.49181
## peak.9 peak.10
## 0.05 14.32509 14.07165plot(cim.2, lty = 1, lwd = 2, incl.mkr = NULL, cex.incl = 0.7,
cex.axis = 0.8, col = "red", ylab = "LOD Score", xlab = "Linkage Group",
main = "CIM - WAB_20min_mean", ylim = c(0,20))
abline(h = summary(cim.perm.2, alpha = 0.05)[1,2], col = "blue")
## Threshold considering 1000 permutationsAs we can see, there is evidence of putative QTL in the chromosomes 4 and 15 for WAB_20min_mean. Let’s check if it was placed in any position between two possible SNP markers or exactly in the position of one SNP.
## lg pos.cM LOD model
## loc207 1 207.0000 14.42392 0
## chr02_15908191 2 103.1653 9.06600 0
## loc337 3 337.0000 15.11768 0
## loc182 4 182.0000 17.60782 0
## loc346 5 346.0000 16.48064 0
## loc756 6 756.0000 15.92880 0
## loc1394 7 1394.0000 16.36499 0
## loc889 8 889.0000 15.55466 0
## loc712 9 712.0000 16.11901 0
## loc1940 10 1940.0000 16.02282 0
## loc212 11 212.0000 15.51646 0
## loc1210 12 1210.0000 15.96749 0
## loc790 13 790.0000 11.81998 0
## loc595 14 595.0000 15.96103 0
## loc797 15 797.0000 17.14522 0Both putative QTL are located at the position between markers
(loc182 and loc797 for 4 and 15,
respectively). We can print their effects:
## chr04_2114468-chr04_2206358
## LG 4.000000e+00
## pos 1.820000e+02
## -log10(pval) 1.676440e+01
## LOD_Ha 1.760783e+01
## mu 2.991835e+00
## alpha_p -7.490161e-02
## LOD_H1 5.494203e+00
## alpha_q -2.732084e-01
## LOD_H2 9.411112e+00
## delta_pq -4.382767e-01
## LOD_H3 8.905254e+00
## H4_pvalue 1.272209e-10
## H5_pvalue 7.267514e-09
## H6_pvalue 1.908264e-11
## model 0.000000e+00
## attr(,"class")
## [1] "fullsib_char" "matrix"The putative QTL in the chromosome 4 is located between the SNP
markers chr04_2114468 and chr04_2206358. Its
effect in the phenotypic variation of WAB_20min_mean is due to three
statistically significant genetic effects: additive effects of the
parents p (\(\alpha_{p} =
-0.075\)) and q (\(\alpha_{q} = -0.273\)) and dominance effect
involving the alleles of both parents (\(\delta_{pq} = -0.438\)). As we can see, the
present QTL contributes to a reduction of the phenotypic values
regarding the trait WAB_20min_mean.
## chr15_26664415-chr15_26850424
## LG 1.500000e+01
## pos 7.970000e+02
## -log10(pval) 1.630698e+01
## LOD_Ha 1.714526e+01
## mu 2.991835e+00
## alpha_p 4.320198e-01
## LOD_H1 4.810967e+00
## alpha_q -2.762057e-01
## LOD_H2 8.611095e+00
## delta_pq 7.698683e-02
## LOD_H3 8.120867e+00
## H4_pvalue 1.711155e-12
## H5_pvalue 2.289893e-12
## H6_pvalue 4.988473e-15
## model 0.000000e+00
## attr(,"class")
## [1] "fullsib_char" "matrix"As it was verified to the QTL on chr4, the putative QTL located in
the chr15 also presented three statistically significant genetic
effects: additive effects of the parents p (\(\alpha_{p} = 0.432\)) and q
(\(\alpha_{q} = -0.276\)) and dominance
effect involving the alleles of both parents (\(\delta_{pq} = 0.077\)). However, while the
parent q (VEN25) contributes to a reduction of the
phenotypic values for the trait WAB_20min_mean, the parent
p (SM3759-36) contributes to a increase of the phenotypic
values. Moreover, the variation of this trait also can be explained by
the dominance effect between the alleles of both parents.
QTL segregation:
## QTL segregation is 1:1:1:1## QTL segregation is 1:1:1:1Map with QTL:
##
## Printing QTL and its linkage phase between markers across the LG:
##
## Markers Position Parent 1 Parent 2
##
## chr04_391747 0.00 a | | b a | | a
## chr04_891244 0.00 b | | a a | | a
## chr04_911675 0.00 b | | a a | | a
## chr04_1268887 0.00 b | | a a | | a
## chr04_1322698 0.00 a | | a a | | b
## chr04_1704199 0.01 b | | a a | | a
## chr04_1795042 0.01 b | | a a | | a
## chr04_1954591 40.68 a | | a b | | a
## chr04_2080316 40.68 a | | a a | | b
## chr04_2114468 110.28 a | | a a | | b
## QTL 182.00 P2 | | P1 Q2 | | Q1
## chr04_2206358 398.11 a | | a a | | b
## chr04_2364616 398.11 a | | b a | | a
## chr04_2475289 398.11 a | | a b | | a
## chr04_2794372 398.11 a | | b a | | a
## chr04_3003258 398.11 a | | b a | | a
## chr04_3014960 398.11 a | | b a | | b
## chr04_3086063 398.11 a | | b a | | a
## chr04_3090271 398.11 a | | b a | | a
## chr04_3299710 400.04 a | | b a | | a
## chr04_3374712 403.05 a | | a b | | a
## chr04_3587533 403.05 a | | b a | | a
## chr04_3799426 403.59 b | | a a | | a
## chr04_3811628 403.59 b | | a a | | b
## chr04_4509026 406.27 a | | a a | | b
## chr04_4523721 406.28 a | | a a | | b
## chr04_4524707 406.28 a | | a a | | b
## chr04_4550004 406.28 a | | a a | | b
## chr04_5416953 411.90 b | | a a | | a
## chr04_5839994 412.94 b | | a a | | a
## chr04_6013207 412.95 b | | a a | | a
## chr04_6055699 412.95 b | | a a | | a
## chr04_6499259 414.96 a | | b a | | a
## chr04_6501866 415.10 a | | a b | | a
## chr04_6772097 415.10 a | | a a | | b
## chr04_6893984 415.10 a | | a b | | a
## chr04_6934110 415.10 a | | a b | | a
## chr04_7310045 431.38 b | | a a | | b
## chr04_7502783 475.64 b | | a a | | a
## chr04_7955069 763.46 b | | a a | | a
## chr04_8125737 763.46 a | | a b | | a
## chr04_8157485 763.89 a | | b a | | a
## chr04_8568037 771.64 a | | a a | | b
## chr04_8806299 771.64 b | | a b | | a
## chr04_8875169 773.57 a | | b a | | a
## chr04_9029859 773.57 a | | a a | | b
## chr04_9293787 773.57 a | | b a | | a
## chr04_9390964 773.57 b | | a b | | a
## chr04_9420463 773.57 a | | b a | | a
## chr04_9438154 774.48 a | | a a | | b
## chr04_9894118 774.48 b | | a b | | a
## chr04_10201069 774.49 a | | b a | | a
## chr04_10251505 774.49 b | | a a | | a
## chr04_10255496 774.49 a | | a a | | b
## chr04_10340200 774.49 b | | a a | | a
## chr04_10493198 789.14 a | | a a | | b
## chr04_10518975 791.21 a | | a b | | a
## chr04_10750938 791.21 a | | a a | | b
## chr04_10926116 791.21 a | | a a | | b
## chr04_11724660 791.21 a | | b a | | a
## chr04_12729683 791.22 a | | a a | | b
## chr04_12962635 835.19 a | | a a | | b
## chr04_13533082 835.19 a | | a a | | b
## chr04_15442203 877.77 a | | a a | | b
## chr04_15545878 877.77 a | | a a | | b
## chr04_15596623 877.77 a | | a a | | b
## chr04_16352399 877.77 a | | a a | | b
## chr04_16759174 877.77 a | | a a | | b
## chr04_16793581 877.77 a | | a a | | b
## chr04_16828066 877.78 a | | a a | | b
## chr04_16836542 877.97 a | | a a | | b
## chr04_17087847 883.69 a | | b b | | a
## chr04_17228124 900.81 b | | a a | | a
## chr04_17452133 900.81 a | | a a | | b
## chr04_17461424 911.32 a | | b a | | b
## chr04_17474173 1199.15 a | | a a | | b
## chr04_17637104 1486.97 a | | a b | | a
## chr04_17714600 1486.97 a | | a b | | a
## chr04_17972545 1486.97 a | | a b | | a
## chr04_18233860 1488.64 a | | a b | | a
## chr04_19064008 1488.65 a | | a b | | a
## chr04_19458348 1488.65 a | | a b | | a
## chr04_19463602 1488.65 a | | a b | | a
## chr04_19671995 1488.65 a | | b b | | a
## chr04_19734995 1492.68 a | | a a | | b
## chr04_19915217 1492.68 a | | a a | | b
## chr04_20066383 1492.68 a | | b a | | a
## chr04_20069290 1492.68 a | | a b | | a
## chr04_20678178 1511.54 b | | a b | | a
## chr04_21200976 1511.54 b | | a a | | a
## chr04_21244611 1511.54 a | | b a | | a
## chr04_21355658 1511.55 a | | b a | | a
## chr04_21416701 1511.55 a | | b a | | a
## chr04_21644206 1511.55 b | | a a | | a
## chr04_21724718 1513.24 a | | b a | | a
## chr04_21968392 1517.25 a | | a a | | b
## chr04_22277601 1517.25 b | | a b | | a
## chr04_22366147 1517.25 a | | b a | | a
## chr04_22371492 1517.25 a | | b a | | a
## chr04_22424131 1517.25 b | | a a | | a
## chr04_22950786 1518.73 b | | a a | | a
## chr04_23086340 1518.73 a | | b a | | a
## chr04_23130237 1518.73 a | | b a | | a
## chr04_23365062 1518.73 b | | a b | | a
## chr04_23378695 1518.74 a | | a b | | a
## chr04_23526802 1533.20 a | | a b | | a
## chr04_23743905 1533.20 a | | b a | | a
## chr04_24584238 1533.20 a | | b a | | a
## chr04_24895121 1533.20 b | | a b | | a
## chr04_24918640 1540.30 b | | a b | | a
## chr04_25173983 1540.31 a | | b a | | a
## chr04_25369192 1690.33 b | | a a | | a
## chr04_26017083 1978.15 b | | a a | | a
## chr04_26114755 1978.15 b | | a a | | a
## chr04_26137640 1978.15 b | | a a | | a
## chr04_26150871 1978.47 b | | a a | | a
## chr04_26762841 1979.18 a | | a b | | a
## chr04_26940560 2032.96 b | | a b | | a
## chr04_27093403 2100.03 a | | b a | | b
## chr04_27125908 2100.03 b | | a a | | a
## chr04_27349432 2100.03 a | | b a | | a
## chr04_27401802 2100.03 a | | b a | | a
## chr04_27512763 2101.56 b | | a a | | b
## chr04_27698652 2101.62 a | | a b | | a
## chr04_27945958 2103.37 a | | b a | | b
## chr04_28075041 2103.37 a | | b a | | a
## chr04_28076948 2103.37 a | | b a | | a
## chr04_28096572 2103.37 a | | b a | | b
## chr04_28144001 2106.55 a | | b a | | a
## chr04_28528714 2106.55 a | | a a | | b
## chr04_28614311 2106.55 a | | a a | | b
## chr04_28645833 2106.55 b | | a a | | a
## chr04_29038920 2109.70 b | | a a | | a
## chr04_29063249 2109.70 b | | a a | | a
## chr04_29085815 2109.70 a | | b a | | a
## chr04_29268590 2109.70 a | | a b | | a
## chr04_29326411 2109.71 b | | a b | | a
## chr04_29444855 2109.71 a | | a b | | a
## chr04_29567368 2109.71 a | | b a | | a
## chr04_29601489 2110.51 a | | a a | | b
## chr04_29732683 2110.51 b | | a a | | a
## chr04_29756282 2110.51 b | | a a | | a
## chr04_29846138 2110.51 a | | a a | | b
## chr04_30765644 2117.73 b | | a a | | b
## chr04_30765746 2117.73 b | | a a | | b
## chr04_30934834 2117.73 b | | a a | | a
## chr04_30978906 2117.73 b | | a a | | a
## chr04_31010792 2117.74 b | | a a | | a
## chr04_31168495 2118.82 a | | b a | | a
## chr04_31757846 2118.82 b | | a a | | a
## chr04_32043984 2118.83 b | | a a | | a
## chr04_32110281 2118.83 b | | a b | | a
## chr04_32294542 2120.51 b | | a a | | a
## chr04_33191652 2120.51 b | | a a | | a
## chr04_33318959 2120.51 a | | b a | | a
## chr04_33897902 2120.51 a | | b a | | a
## chr04_34311548 2122.45 b | | a a | | a
## chr04_34617798 2124.29 b | | a a | | b
## chr04_34705212 2124.29 b | | a a | | b
## chr04_34791861 2124.29 a | | b b | | a
## chr04_35017677 2124.30 a | | b b | | a
## chr04_35103588 2124.30 a | | b a | | a
## chr04_35116226 2124.30 a | | b a | | a
## chr04_35281540 2124.30 a | | b a | | a
## chr04_35307115 2124.30 a | | b a | | a
## chr04_35444620 2124.30 a | | b a | | a
##
## P1 and Q1 have positive effect (increase phenotypic value)
## P2 and Q2 have negative effect (reduce phenotypic value)
## P0 and Q0 have neutral effect (non signif.)##
## Printing QTL and its linkage phase between markers across the LG:
##
## Markers Position Parent 1 Parent 2
##
## chr15_271280 0.00 a | | b a | | a
## chr15_454604 0.00 b | | a a | | a
## chr15_639046 0.00 a | | b a | | a
## chr15_759305 0.00 b | | a a | | b
## chr15_1008956 1.04 b | | a a | | b
## chr15_1212302 1.04 a | | b a | | a
## chr15_1984742 5.45 a | | b a | | a
## chr15_2035494 5.45 a | | b a | | a
## chr15_2189555 5.46 a | | b a | | a
## chr15_2211351 5.46 b | | a a | | a
## chr15_2487620 5.46 a | | a b | | a
## chr15_2947559 5.83 a | | a b | | a
## chr15_2970124 5.84 a | | a a | | b
## chr15_3045621 5.84 b | | a a | | a
## chr15_3047184 7.21 a | | a b | | a
## chr15_3315248 7.21 a | | a b | | a
## chr15_3354111 7.21 a | | a b | | a
## chr15_3354574 7.21 a | | a a | | b
## chr15_3368785 8.55 a | | a a | | b
## chr15_3422890 8.55 a | | a a | | b
## chr15_3456558 9.96 a | | a a | | b
## chr15_3529081 9.96 a | | a a | | b
## chr15_3579350 9.96 a | | a a | | b
## chr15_3659490 9.96 a | | b a | | a
## chr15_3678288 9.96 a | | a b | | a
## chr15_3868181 12.55 a | | a b | | a
## chr15_3989260 12.55 a | | a b | | a
## chr15_4428255 16.59 a | | b a | | a
## chr15_4607587 16.59 a | | b a | | a
## chr15_4643713 16.60 a | | b a | | a
## chr15_4831192 16.60 a | | b a | | a
## chr15_4885004 16.60 a | | a b | | a
## chr15_5046529 16.60 a | | a a | | b
## chr15_5140570 16.60 a | | a a | | b
## chr15_5516003 17.69 b | | a b | | a
## chr15_5537118 17.69 a | | b a | | a
## chr15_5656754 17.70 a | | a b | | a
## chr15_5708587 17.70 a | | a a | | b
## chr15_6069850 17.70 b | | a b | | a
## chr15_6404222 20.30 b | | a a | | a
## chr15_6794892 20.30 a | | b a | | a
## chr15_6814624 20.30 a | | b a | | a
## chr15_6856903 20.30 a | | b a | | a
## chr15_6862817 20.30 a | | b a | | a
## chr15_7017726 20.30 a | | b a | | a
## chr15_7051194 20.30 a | | b a | | a
## chr15_7306447 25.49 a | | a a | | b
## chr15_7525298 25.49 b | | a a | | a
## chr15_7566174 25.49 b | | a a | | b
## chr15_7570106 25.49 b | | a a | | a
## chr15_7727909 26.37 b | | a a | | b
## chr15_7966405 26.37 a | | a b | | a
## chr15_8178128 26.65 a | | a a | | b
## chr15_8515738 28.73 b | | a a | | a
## chr15_8529544 28.73 b | | a a | | a
## chr15_8643402 28.74 b | | a b | | a
## chr15_8663652 28.74 b | | a a | | a
## chr15_8778378 31.27 a | | b a | | a
## chr15_8839341 31.27 a | | b a | | a
## chr15_9411816 34.00 b | | a a | | a
## chr15_9495715 34.01 b | | a a | | a
## chr15_9708158 34.01 a | | b a | | a
## chr15_10203363 37.66 b | | a a | | a
## chr15_10215356 41.55 b | | a b | | a
## chr15_10241563 41.55 a | | a b | | a
## chr15_10357956 41.55 b | | a a | | a
## chr15_10366649 41.55 b | | a a | | a
## chr15_10478650 41.55 b | | a a | | a
## chr15_10524104 42.66 b | | a a | | a
## chr15_10562738 42.66 b | | a a | | a
## chr15_10764851 42.66 a | | b a | | a
## chr15_10870334 42.66 a | | b a | | a
## chr15_11095252 42.66 a | | b a | | a
## chr15_11115852 42.66 a | | a a | | b
## chr15_11118493 42.66 a | | a a | | b
## chr15_11200981 42.66 a | | b a | | a
## chr15_11461978 42.67 a | | b a | | a
## chr15_11536113 42.67 b | | a a | | a
## chr15_11626152 44.01 a | | b a | | a
## chr15_11880785 50.84 a | | a b | | a
## chr15_12014457 50.85 a | | a b | | a
## chr15_12123543 50.85 a | | a b | | a
## chr15_12399373 51.37 b | | a a | | a
## chr15_12432085 51.38 b | | a a | | a
## chr15_12532293 62.06 a | | a b | | a
## chr15_12742992 70.88 a | | a a | | b
## chr15_12765366 70.88 a | | a a | | b
## chr15_13251260 71.93 a | | b a | | a
## chr15_13282944 71.93 a | | b a | | a
## chr15_13364229 75.30 a | | b a | | a
## chr15_13423862 75.30 b | | a b | | a
## chr15_13586374 79.14 a | | b a | | b
## chr15_13695004 79.15 a | | b a | | a
## chr15_13720338 79.16 a | | b a | | a
## chr15_14569055 85.97 a | | a b | | a
## chr15_14841097 85.97 b | | a b | | a
## chr15_15440022 89.14 a | | a a | | b
## chr15_15467255 89.14 b | | a b | | a
## chr15_15495127 89.14 b | | a b | | a
## chr15_15734989 89.14 b | | a a | | a
## chr15_15812320 89.14 a | | b a | | a
## chr15_15874896 89.14 a | | b a | | a
## chr15_16217472 89.14 a | | a a | | b
## chr15_16526556 89.14 a | | a a | | b
## chr15_16981238 102.77 a | | a b | | a
## chr15_17399070 102.78 a | | a b | | a
## chr15_17878096 122.11 a | | a a | | b
## chr15_17916467 122.11 a | | a a | | b
## chr15_17923623 122.11 a | | a b | | a
## chr15_17963582 122.11 a | | a a | | b
## chr15_18518397 122.11 a | | a a | | b
## chr15_18604125 122.11 a | | a b | | a
## chr15_18722955 122.11 b | | a a | | a
## chr15_18952419 122.11 a | | a a | | b
## chr15_19239977 122.12 a | | a a | | b
## chr15_20029365 122.12 a | | a b | | a
## chr15_20279347 124.08 a | | a a | | b
## chr15_20708177 124.08 a | | a a | | b
## chr15_20747781 124.08 a | | a b | | a
## chr15_20968221 124.09 a | | a a | | b
## chr15_21099064 124.09 a | | a a | | b
## chr15_21179453 124.09 b | | a a | | a
## chr15_21243102 124.09 a | | a a | | b
## chr15_21372134 124.09 a | | a a | | b
## chr15_21380306 124.09 b | | a a | | a
## chr15_21623213 124.09 a | | a b | | a
## chr15_21869765 137.72 a | | b a | | b
## chr15_22615384 171.01 b | | a a | | a
## chr15_22721386 171.01 b | | a b | | a
## chr15_22826481 171.02 b | | a a | | a
## chr15_22828132 171.02 b | | a a | | a
## chr15_22844506 171.02 a | | a b | | a
## chr15_23038891 171.02 b | | a a | | a
## chr15_23041036 171.02 a | | a b | | a
## chr15_23286975 171.02 b | | a b | | a
## chr15_23634541 207.55 a | | b a | | a
## chr15_23704580 251.67 a | | b b | | a
## chr15_23709517 260.53 b | | a a | | a
## chr15_23813603 260.53 a | | a a | | b
## chr15_24354106 260.53 a | | a a | | b
## chr15_24449203 263.68 a | | a a | | b
## chr15_24796764 263.68 a | | a a | | b
## chr15_24798440 263.68 a | | a a | | b
## chr15_24967848 263.68 a | | a a | | b
## chr15_25005084 263.68 a | | b a | | a
## chr15_25016180 263.68 a | | a a | | b
## chr15_25221286 263.68 a | | b a | | a
## chr15_25758711 263.69 a | | a a | | b
## chr15_25815423 263.69 a | | b a | | a
## chr15_25906435 263.69 a | | a a | | b
## chr15_25917240 263.69 a | | a a | | b
## chr15_25917999 263.69 a | | a a | | b
## chr15_26048540 322.12 b | | a a | | b
## chr15_26075697 356.08 b | | a a | | b
## chr15_26162406 356.08 b | | a a | | b
## chr15_26204102 356.08 b | | a a | | b
## chr15_26241587 356.08 b | | a a | | b
## chr15_26362187 356.08 a | | a b | | a
## chr15_26566456 356.08 a | | b b | | a
## chr15_26588779 643.90 a | | a b | | a
## chr15_26639349 666.97 a | | a b | | a
## chr15_26641627 682.64 a | | b a | | a
## chr15_26664415 699.74 a | | a a | | b
## QTL 797.00 P1 | | P2 Q2 | | Q1
## chr15_26850424 987.56 a | | b a | | a
## chr15_26964927 987.56 a | | a b | | a
## chr15_27010753 991.05 b | | a a | | b
## chr15_27011249 991.05 a | | b a | | a
## chr15_27121653 991.05 b | | a a | | a
## chr15_27480375 991.05 b | | a a | | b
## chr15_27500520 991.05 a | | b a | | a
## chr15_27746732 991.05 a | | b a | | a
## chr15_27972388 991.05 a | | a b | | a
## chr15_28026753 991.05 a | | b b | | a
## chr15_28062643 991.05 a | | a b | | a
## chr15_28207478 991.05 b | | a a | | b
## chr15_28210857 991.06 a | | b a | | a
## chr15_28211421 991.06 a | | b a | | a
## chr15_28245269 991.06 b | | a a | | b
## chr15_28267633 992.10 b | | a a | | b
## chr15_28437366 992.10 a | | b a | | a
## chr15_28460156 992.10 a | | a b | | a
## chr15_28495335 992.10 b | | a b | | a
## chr15_28502884 992.10 a | | a b | | a
## chr15_28613174 992.50 b | | a a | | a
## chr15_28618198 992.57 a | | a b | | a
## chr15_28674406 992.57 a | | a a | | b
## chr15_28833496 992.57 a | | b a | | a
## chr15_28908060 992.57 a | | b a | | a
## chr15_28924808 992.58 b | | a a | | a
## chr15_29098245 992.58 a | | b a | | a
## chr15_29169176 992.58 a | | a b | | a
## chr15_29239524 992.58 a | | b a | | a
## chr15_29304144 992.58 a | | b a | | a
## chr15_29746090 992.58 a | | b a | | a
## chr15_29839578 992.58 b | | a a | | b
## chr15_29907707 992.58 a | | a a | | b
## chr15_29950018 992.58 a | | a b | | a
## chr15_30497931 992.58 b | | a a | | a
## chr15_30729260 992.59 a | | a b | | a
## chr15_30768089 992.59 a | | b a | | a
## chr15_31498760 992.59 a | | b a | | a
## chr15_31547691 992.59 b | | a b | | a
## chr15_31564667 992.59 a | | b a | | a
## chr15_31957888 992.59 b | | a b | | a
##
## P1 and Q1 have positive effect (increase phenotypic value)
## P2 and Q2 have negative effect (reduce phenotypic value)
## P0 and Q0 have neutral effect (non signif.)Proportion of variation explained by the QTL:
## lg loc r2
## R2.trait NA All 17.0636
## R2.lg4.loc182 4 loc182 17.0636## lg loc r2
## R2.trait NA All 11.9463
## R2.lg15.loc797 15 loc797 11.9463The putative QTL located in the chromosomes 4 and 15 explain \(17.06\%\) and \(11.95\%\) of WAB_20min_mean variation in the background of fullsib family 159 (SM3759-36 x VEN25).
WAB_30min_mean:
Cofactor selection:
## Number of Cofactors selected: 1 ... 2 ... 3 ... 4 ... 5 ... done
Genome scan:
## QTL mapping for 18 groups
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Permutation test:
cim.perm.3 <- cim_scan(fullsib = cofs.fs.3, lg = "all", pheno.col = 3, LOD = TRUE, n.perm = 1000,
write.perm = "CIM_Permutations_WAB_30min_mean.txt")
save(cim.perm.3, file = "CIM_Permutations_WAB_30min_mean.RData")## Threshold considering 1000 permutations## peak.1 peak.2 peak.3 peak.4 peak.5 peak.6 peak.7 peak.8
## 0.05 16.74053 15.52238 14.94398 14.38068 14.08942 13.62044 13.40852 13.20061
## peak.9 peak.10
## 0.05 12.89098 12.72261plot(cim.3, lty = 1, lwd = 2, incl.mkr = NULL, cex.incl = 0.7,
cex.axis = 0.8, col = "red", ylab = "LOD Score", xlab = "Linkage Group",
main = "CIM - WAB_30min_mean", ylim = c(0,20))
abline(h = summary(cim.perm.3, alpha = 0.05)[1,2], col = "blue")
## Threshold considering 1000 permutationsAs we can see, no QTL was detected for the trait WAB_30min_mean in the fullsib family 159 (SM3759-36 x VEN25).
Genome-Wide Association Study (GWAS)
To perform association mapping, we need to extract the genotypic data from the original VCF file:
vcf.file.P1.geno <- extract.gt(vcf.file.P1, element = "GT", as.numeric = FALSE, return.alleles = FALSE,
IDtoRowNames = TRUE, extract = TRUE, convertNA = FALSE)
vcf.file.P1.geno[1:5,1:5]## 202023_100_PER183 202023_101_GM13174-48 202023_102_GM13174-49
## chr09_36007461 "0/0" "1/1" "1/1"
## chr10_20626079 "1/1" "0/1" "0/1"
## chr12_28750390 "0/0" "0/0" "0/0"
## chr17_1253873 "0/1" "0/0" "0/0"
## chr15_25005084 "0/0" "0/0" "0/0"
## 202023_103_GM13174-50 202023_104_GM13174-51
## chr09_36007461 "1/1" "1/1"
## chr10_20626079 "0/1" "0/1"
## chr12_28750390 "0/0" "0/0"
## chr17_1253873 "0/1" "0/1"
## chr15_25005084 "0/0" "1/1"vcf.file.P1.geno <- apply(vcf.file.P1.geno, 2, function(x) unlist(lapply(strsplit(x, split = ""),
function(y) paste(y[c(1,3)],
collapse = ""))))
for(i in 1:nrow(vcf.file.P1.geno))
{
vcf.file.P1.geno[i,][which(vcf.file.P1.geno[i,] == "..")] <- NA
}
vcf.file.P1.geno[1:5,1:5]## 202023_100_PER183 202023_101_GM13174-48 202023_102_GM13174-49
## chr09_36007461 "00" "11" "11"
## chr10_20626079 "11" "01" "01"
## chr12_28750390 "00" "00" "00"
## chr17_1253873 "01" "00" "00"
## chr15_25005084 "00" "00" "00"
## 202023_103_GM13174-50 202023_104_GM13174-51
## chr09_36007461 "11" "11"
## chr10_20626079 "01" "01"
## chr12_28750390 "00" "00"
## chr17_1253873 "01" "01"
## chr15_25005084 "00" "11"Let’s check which genotypes were really phenotyped for the traits and only use them to perform GWAS:
## genotype DM_NIRS WAB_20min_mean WAB_30min_mean
## 1 202023_1_GM13159-1 34.44704 0.9350957 3.384388
## 2 202023_10_COL1722 46.70277 5.6381474 11.787712
## 3 202023_100_PER183 36.91860 3.8691301 12.844435
## 4 202023_101_GM13174-48 32.51729 0.6115424 2.282428
## 5 202023_102_GM13174-49 39.80332 2.4799337 4.255318
## 6 202023_103_GM13174-50 33.85145 0.9029387 2.813686corresp <- colnames(vcf.file.P1.geno) %in% BLUEs.DM.WAB$genotype
vcf.file.P1.geno.DM.WAB <- vcf.file.P1.geno[,which(corresp == TRUE)]
dim(vcf.file.P1.geno.DM.WAB)## [1] 9000 220Since we will use a numeric format (0, 1, 2) for the genotypic data,
it is need to convert the original data from a VCF file. The users can
transform the data by their own and also filter them for some
parameters, such as minor allele frequency (MAF), number of genotypes
and markers to be considered following some proportion of missing data,
and so on. Moreover, a imputation can be done for missing data. For now,
we will transform and filter genotypic data for the numeric format using
the R package statgenGWAS.
## [1] 220 9000## chr09_36007461 chr10_20626079 chr12_28750390
## 202023_100_PER183 "00" "11" "00"
## 202023_101_GM13174-48 "11" "01" "00"
## 202023_102_GM13174-49 "11" "01" "00"
## 202023_103_GM13174-50 "11" "01" "00"
## 202023_104_GM13174-51 "11" "01" "00"
## chr17_1253873 chr15_25005084
## 202023_100_PER183 "01" "00"
## 202023_101_GM13174-48 "00" "00"
## 202023_102_GM13174-49 "00" "00"
## 202023_103_GM13174-50 "01" "00"
## 202023_104_GM13174-51 "01" "11"vcf.file.P1.geno.DM.WAB <- vcf.file.P1.geno.DM.WAB[,order(colnames(vcf.file.P1.geno.DM.WAB))]
vcf.file.P1.geno.DM.WAB[1:5,1:5]## chr01_10004826 chr01_10151663 chr01_10367331
## 202023_100_PER183 "01" "00" "00"
## 202023_101_GM13174-48 "01" "00" "00"
## 202023_102_GM13174-49 "01" "00" "00"
## 202023_103_GM13174-50 "01" "00" "00"
## 202023_104_GM13174-51 "01" "00" "00"
## chr01_10386693 chr01_10394249
## 202023_100_PER183 "00" "00"
## 202023_101_GM13174-48 "01" "01"
## 202023_102_GM13174-49 "01" "01"
## 202023_103_GM13174-50 "01" "01"
## 202023_104_GM13174-51 "00" "00"# Map:
spliting.names <- strsplit(colnames(vcf.file.P1.geno.DM.WAB), split = "")
chr <- unlist(lapply(spliting.names, function(x) paste(x[(which(x == "_")-2):(which(x == "_")-1)],
collapse = "")))
pos <- unlist(lapply(spliting.names, function(x) paste(x[(which(x == "_")+1):(length(x))],
collapse = "")))
table(chr)## chr
## 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18
## 598 605 456 522 452 475 547 546 485 435 473 585 535 460 488 470 449 419## chr pos
## 1 01 10004826
## 2 01 10151663
## 3 01 10367331
## 4 01 10386693
## 5 01 10394249
## 6 01 10459214## [1] 9000 2## chr pos
## chr01_10004826 01 10004826
## chr01_10151663 01 10151663
## chr01_10367331 01 10367331
## chr01_10386693 01 10386693
## chr01_10394249 01 10394249
## chr01_10459214 01 10459214## 'data.frame': 9000 obs. of 2 variables:
## $ chr: chr "01" "01" "01" "01" ...
## $ pos: chr "10004826" "10151663" "10367331" "10386693" ...cassava.data.coded <- codeMarkers(gData = cassava.data, refAll = rep("0", times = ncol(vcf.file.P1.geno.DM.WAB)),
nMissGeno = 0.40, nMiss = 0.50, impute = TRUE, imputeType = "fixed", fixedValue = 1,
MAF = 0.05, removeDuplicates = TRUE, verbose = TRUE)## Input contains 9000 SNPs for 220 genotypes.
## 0 genotypes removed because proportion of missing values larger than or equal to 0.4.
## 0 SNPs removed because proportion of missing values larger than or equal to 0.5.
## 1141 SNPs removed because MAF smaller than 0.05.
## 98 duplicate SNPs removed.
## 23069 missing values imputed.
## 0 SNPs removed because MAF smaller than 0.05 after imputation.
## 9 duplicate SNPs removed after imputation.
## Output contains 7752 SNPs for 220 genotypes.With the genotypic data in numeric format, we can prepare the files
for running GWAS in the R package rMVP.
## [1] 220 7752## chr01_10004826 chr01_10151663 chr01_10367331
## 202023_1_GM13159-1 0 2 2
## 202023_10_COL1722 0 2 0
## 202023_100_PER183 1 2 2
## 202023_101_GM13174-48 1 2 2
## 202023_102_GM13174-49 1 2 2
## chr01_10386693 chr01_10394249
## 202023_1_GM13159-1 1 2
## 202023_10_COL1722 1 2
## 202023_100_PER183 2 2
## 202023_101_GM13174-48 1 1
## 202023_102_GM13174-49 1 1## [1] 7752 220## 202023_1_GM13159-1 202023_10_COL1722 202023_100_PER183
## chr01_10004826 0 0 1
## chr01_10151663 2 2 2
## chr01_10367331 2 0 2
## chr01_10386693 1 1 2
## chr01_10394249 2 2 2
## 202023_101_GM13174-48 202023_102_GM13174-49
## chr01_10004826 1 1
## chr01_10151663 2 2
## chr01_10367331 2 2
## chr01_10386693 1 1
## chr01_10394249 1 1## [1] 9000 2## chr pos
## chr01_10004826 01 10004826
## chr01_10151663 01 10151663
## chr01_10367331 01 10367331
## chr01_10386693 01 10386693
## chr01_10394249 01 10394249
## chr01_10459214 01 10459214map.for.rMVP$Pos <- map.for.rMVP$pos
map.for.rMVP <- map.for.rMVP[,-2]
map.for.rMVP$SNP <- rownames(map.for.rMVP)
map.for.rMVP <- map.for.rMVP[rownames(data.for.rMVP),]
rownames(map.for.rMVP) <- 1:nrow(map.for.rMVP)
colnames(map.for.rMVP)[1] <- "Chr"
map.for.rMVP <- map.for.rMVP[,c("SNP","Chr","Pos")]
head(map.for.rMVP)## SNP Chr Pos
## 1 chr01_10004826 01 10004826
## 2 chr01_10151663 01 10151663
## 3 chr01_10367331 01 10367331
## 4 chr01_10386693 01 10386693
## 5 chr01_10394249 01 10394249
## 6 chr01_10459214 01 10459214## 'data.frame': 7752 obs. of 3 variables:
## $ SNP: chr "chr01_10004826" "chr01_10151663" "chr01_10367331" "chr01_10386693" ...
## $ Chr: chr "01" "01" "01" "01" ...
## $ Pos: num 10004826 10151663 10367331 10386693 10394249 ...## [1] 7752 3For a suitable reading of data from R package rMVP, it
is needed to save the file as a big matrix:
## 202023_1_GM13159-1 202023_10_COL1722 202023_100_PER183
## chr01_10004826 0 0 1
## chr01_10151663 2 2 2
## chr01_10367331 2 0 2
## chr01_10386693 1 1 2
## chr01_10394249 2 2 2
## 202023_101_GM13174-48 202023_102_GM13174-49
## chr01_10004826 1 1
## chr01_10151663 2 2
## chr01_10367331 2 2
## chr01_10386693 1 1
## chr01_10394249 1 1## 202023_1_GM13159-1 202023_10_COL1722 202023_100_PER183
## chr01_10004826 0 0 1
## chr01_10151663 2 2 2
## chr01_10367331 2 0 2
## chr01_10386693 1 1 2
## chr01_10394249 2 2 2
## 202023_101_GM13174-48 202023_102_GM13174-49
## chr01_10004826 1 1
## chr01_10151663 2 2
## chr01_10367331 2 2
## chr01_10386693 1 1
## chr01_10394249 1 1Preparing files in the R package rMVP for run GWAS:
MVP.Data(fileNum = "Data_For_rMVP.txt", filePhe = "BLUEs_DM_WAB.csv", fileMap = "Map_For_rMVP.txt",
sep.num = "\t", sep.phe = ",", sep.map = "\t", SNP.impute = NULL, fileKin = FALSE, filePC = FALSE,
out = "mvp", priority = "speed", verbose = TRUE)## Preparing data for MVP...
## Reading file...
## inds: 220 markers:7752
## Preparation for GENOTYPE data is done within 2s
## Preparation for PHENOTYPE data is Done within 0s
## MVP data prepration accomplished successfully!MVP.Data.Kin(fileKin = TRUE, sep = "\t", mvp_prefix = "mvp", out = "mvp",
priority = "speed", verbose = TRUE)## Calculate KINSHIP using Vanraden method...
## Computing GRM under mode: Speed
## Scale the genotype matrix
## Computing Z'Z
## Deriving relationship matrix successfully
## Preparation for Kinship matrix is done!## An object of class "big.matrix"
## Slot "address":
## <pointer: 0x5627f03c7cd0>MVP.Data.PC(filePC = TRUE, sep = "\t", pcs.keep = 5, mvp_prefix = "mvp", out = "mvp",
priority = "speed", verbose = TRUE)## Computing GRM under mode: Speed
## Scale the genotype matrix
## Computing Z'Z
## Deriving relationship matrix successfully
## Eigen Decomposition on GRM
## Deriving PCs successfully
## Preparation for PC matrix is done!Let’s load the files created by R package rMVP in the
current directory:
genotype <- attach.big.matrix("mvp.geno.desc")
phenotype <- read.table("mvp.phe", head = TRUE)
map <- read.table("mvp.geno.map", head = TRUE)
kinship <- attach.big.matrix("mvp.kin.desc")Genomic-based Relationship Matrix (K Matrix)
## Computing GRM under mode: Speed
## Scale the genotype matrix
## Computing Z'Z
## Deriving relationship matrix successfully## [1] 220 220## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.79759277 -0.13057867 -0.09066269 -0.01227824 0.09039744
## [2,] -0.13057867 1.18495572 0.12611138 -0.07782971 -0.09098944
## [3,] -0.09066269 0.12611138 1.28862520 -0.01631098 -0.03729239
## [4,] -0.01227824 -0.07782971 -0.01631098 0.90495299 0.32974942
## [5,] 0.09039744 -0.09098944 -0.03729239 0.32974942 0.88980737rownames(kinship.plot) <- colnames(data.for.rMVP)
colnames(kinship.plot) <- colnames(data.for.rMVP)
heatmap(kinship.plot, Rowv = TRUE, Colv = TRUE)
Population Structure (Q Matrix)
Although the R package rMVP will automatically create a
PCA plot in the current directory, from the run of its GWAS function, we
will build it by ourselves using the principal components calculated by
rMVP. Thus, it will be possible to input and view the
clustering patterns considering particular filtering, such as different
fullsib families, which constitute the first population (P1):
## Computing GRM under mode: Speed
## Scale the genotype matrix
## Computing Z'Z
## Deriving relationship matrix successfully
## Eigen Decomposition on GRM
## Deriving PCs successfully## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.023973749 0.09454377 0.02415545 0.08622249 0.015670244
## [2,] -0.009518504 -0.06762087 0.18683689 -0.09775046 -0.012224385
## [3,] -0.007393545 -0.03154574 0.04407003 -0.01051980 -0.018478677
## [4,] -0.084794346 0.05234778 -0.01680075 -0.07072566 -0.081086945
## [5,] -0.079858265 0.05609353 -0.02496888 -0.06410526 0.006268762
## [6,] -0.080535903 0.05698547 -0.01866969 -0.05947868 0.039488964## [1] 220 5rownames(pca) <- colnames(data.for.rMVP)
colnames(pca) <- c("PC1", "PC2", "PC3", "PC4", "PC5")
head(pca)## PC1 PC2 PC3 PC4
## 202023_1_GM13159-1 0.023973749 0.09454377 0.02415545 0.08622249
## 202023_10_COL1722 -0.009518504 -0.06762087 0.18683689 -0.09775046
## 202023_100_PER183 -0.007393545 -0.03154574 0.04407003 -0.01051980
## 202023_101_GM13174-48 -0.084794346 0.05234778 -0.01680075 -0.07072566
## 202023_102_GM13174-49 -0.079858265 0.05609353 -0.02496888 -0.06410526
## 202023_103_GM13174-50 -0.080535903 0.05698547 -0.01866969 -0.05947868
## PC5
## 202023_1_GM13159-1 0.015670244
## 202023_10_COL1722 -0.012224385
## 202023_100_PER183 -0.018478677
## 202023_101_GM13174-48 -0.081086945
## 202023_102_GM13174-49 0.006268762
## 202023_103_GM13174-50 0.039488964pca <- as.data.frame(pca)
indiv.vcf.P1.pca <- indiv.vcf.P1[which(indiv.vcf.P1$Indiv_VCF %in% rownames(pca) == TRUE),]
indiv.vcf.P1.pca <- indiv.vcf.P1.pca[order(indiv.vcf.P1.pca$Indiv_VCF),]
pca <- pca[order(rownames(pca)),]
head(cbind(indiv.vcf.P1.pca$Indiv_VCF, rownames(pca)), 20)## [,1] [,2]
## [1,] "202023_1_GM13159-1" "202023_1_GM13159-1"
## [2,] "202023_10_COL1722" "202023_10_COL1722"
## [3,] "202023_100_PER183" "202023_100_PER183"
## [4,] "202023_101_GM13174-48" "202023_101_GM13174-48"
## [5,] "202023_102_GM13174-49" "202023_102_GM13174-49"
## [6,] "202023_103_GM13174-50" "202023_103_GM13174-50"
## [7,] "202023_104_GM13174-51" "202023_104_GM13174-51"
## [8,] "202023_105_GM13174-52" "202023_105_GM13174-52"
## [9,] "202023_106_GM13174-53" "202023_106_GM13174-53"
## [10,] "202023_107_GM13174-54" "202023_107_GM13174-54"
## [11,] "202023_108_GM13174-55" "202023_108_GM13174-55"
## [12,] "202023_11_GM13159-10" "202023_11_GM13159-10"
## [13,] "202023_110_COL1910" "202023_110_COL1910"
## [14,] "202023_111_GM13175-1" "202023_111_GM13175-1"
## [15,] "202023_112_GM13175-2" "202023_112_GM13175-2"
## [16,] "202023_113_GM13175-3" "202023_113_GM13175-3"
## [17,] "202023_114_GM13175-4" "202023_114_GM13175-4"
## [18,] "202023_115_GM13175-5" "202023_115_GM13175-5"
## [19,] "202023_116_GM13175-6" "202023_116_GM13175-6"
## [20,] "202023_117_GM13175-7" "202023_117_GM13175-7"## PC1 PC2 PC3 PC4
## 202023_1_GM13159-1 0.023973749 0.09454377 0.02415545 0.08622249
## 202023_10_COL1722 -0.009518504 -0.06762087 0.18683689 -0.09775046
## 202023_100_PER183 -0.007393545 -0.03154574 0.04407003 -0.01051980
## 202023_101_GM13174-48 -0.084794346 0.05234778 -0.01680075 -0.07072566
## 202023_102_GM13174-49 -0.079858265 0.05609353 -0.02496888 -0.06410526
## 202023_103_GM13174-50 -0.080535903 0.05698547 -0.01866969 -0.05947868
## PC5 Family
## 202023_1_GM13159-1 0.015670244 SM3759-36-x-VEN25
## 202023_10_COL1722 -0.012224385 COL1722
## 202023_100_PER183 -0.018478677 PER183
## 202023_101_GM13174-48 -0.081086945 COL1910-x-SM3759-36
## 202023_102_GM13174-49 0.006268762 COL1910-x-SM3759-36
## 202023_103_GM13174-50 0.039488964 COL1910-x-SM3759-36ggplot(pca, aes(PC1, PC2, colour = Family)) + geom_point(size = 4.0) +
scale_color_manual(values = brewer.pal(n = 11, "Paired")) +
xlab("PC1") + ylab("PC2") +
theme(axis.text.x = element_text(angle = 90, size = 25.0),
axis.text.y = element_text(size = 25.0),
strip.text.x = element_text(size = 30.0, face = "bold"),
strip.text.y = element_text(size = 30.0, face = "bold"),
legend.title = element_text(size = 20),
legend.text = element_text(size = 18)) +
theme_bw()
Let’s check the proportion explained by the first three principal
components. We are going to run PCA again using the R function
prcomp, because the function from rMVP does
not provide the variance of these components.
prin.comp <- prcomp(kinship.plot, scale = TRUE)
eig <- get_eigenvalue(prin.comp)
round(sum(eig$variance.percent[1]),1)## [1] 53## [1] 31.2## [1] 91.9
GWAS Analyses
Running GWAS analyses for the traits DM_NIRS,
WAB_20min_mean, and WAB_30min_mean:
GWAS.rMVP <- vector("list", ncol(phenotype)-1)
for(i in 2:ncol(phenotype))
{
GWAS.rMVP[[i-1]] <- MVP(phe = phenotype[,c(1,i)],
geno = genotype,
map = map,
K = kinship,
nPC.GLM = 5,
nPC.MLM = 3,
nPC.FarmCPU = 3,
#CV.GLM = NULL,
#CV.MLM = NULL,
#CV.FarmCPU = NULL,
priority = "speed",
ncpus = 1,
vc.method = "BRENT",
maxLoop = 12,
method.bin = "static",
permutation.threshold = TRUE,
permutation.rep = 1000,
method = c("GLM","MLM","FarmCPU"),
file.output = c("pmap","pmap.signal","plot","log"),
verbose = FALSE)
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## scanning...## PCA plot2d## SNP_Density Plotting## Circular_Manhattan Plotting DM_NIRS.GLM
## Circular_Manhattan Plotting DM_NIRS.MLM
## Circular_Manhattan Plotting DM_NIRS.FarmCPU
## Rectangular_Manhattan Plotting DM_NIRS.GLM## Rectangular_Manhattan Plotting DM_NIRS.MLM## Rectangular_Manhattan Plotting DM_NIRS.FarmCPU## Q_Q Plotting DM_NIRS.GLM## Q_Q Plotting DM_NIRS.MLM## Q_Q Plotting DM_NIRS.FarmCPU## Multracks_Rectangular Plotting DM_NIRS.GLM## Multracks_Rectangular Plotting DM_NIRS.MLM## Multracks_Rectangular Plotting DM_NIRS.FarmCPU## Multraits_Rectangular Plotting...(finished 14%)
Multraits_Rectangular Plotting...(finished 27%)
Multraits_Rectangular Plotting...(finished 40%)
Multraits_Rectangular Plotting...(finished 53%)
Multraits_Rectangular Plotting...(finished 66%)
Multraits_Rectangular Plotting...(finished 78%)
Multraits_Rectangular Plotting...(finished 91%)
Multraits_Rectangular Plotting...(finished 100%)
## Multracks_QQ Plotting DM_NIRS.GLM## Multracks_QQ Plotting DM_NIRS.MLM## Multracks_QQ Plotting DM_NIRS.FarmCPU## Multraits_QQ Plotting DM_NIRS.GLM
## Multraits_QQ Plotting DM_NIRS.MLM
## Multraits_QQ Plotting DM_NIRS.FarmCPU
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## scanning...## PCA plot2d## SNP_Density Plotting## Circular_Manhattan Plotting WAB_20min_mean.GLM
## Circular_Manhattan Plotting WAB_20min_mean.MLM
## Circular_Manhattan Plotting WAB_20min_mean.FarmCPU
## Rectangular_Manhattan Plotting WAB_20min_mean.GLM## Rectangular_Manhattan Plotting WAB_20min_mean.MLM## Rectangular_Manhattan Plotting WAB_20min_mean.FarmCPU## Q_Q Plotting WAB_20min_mean.GLM## Q_Q Plotting WAB_20min_mean.MLM## Q_Q Plotting WAB_20min_mean.FarmCPU## Multracks_Rectangular Plotting WAB_20min_mean.GLM## Multracks_Rectangular Plotting WAB_20min_mean.MLM## Multracks_Rectangular Plotting WAB_20min_mean.FarmCPU## Multraits_Rectangular Plotting...(finished 14%)
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## Multracks_QQ Plotting WAB_20min_mean.GLM## Multracks_QQ Plotting WAB_20min_mean.MLM## Multracks_QQ Plotting WAB_20min_mean.FarmCPU## Multraits_QQ Plotting WAB_20min_mean.GLM
## Multraits_QQ Plotting WAB_20min_mean.MLM
## Multraits_QQ Plotting WAB_20min_mean.FarmCPU
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## scanning...## PCA plot2d## SNP_Density Plotting## Circular_Manhattan Plotting WAB_30min_mean.GLM
## Circular_Manhattan Plotting WAB_30min_mean.MLM
## Circular_Manhattan Plotting WAB_30min_mean.FarmCPU
## Rectangular_Manhattan Plotting WAB_30min_mean.GLM## Rectangular_Manhattan Plotting WAB_30min_mean.MLM## Rectangular_Manhattan Plotting WAB_30min_mean.FarmCPU## Q_Q Plotting WAB_30min_mean.GLM## Q_Q Plotting WAB_30min_mean.MLM## Q_Q Plotting WAB_30min_mean.FarmCPU## Multracks_Rectangular Plotting WAB_30min_mean.GLM## Multracks_Rectangular Plotting WAB_30min_mean.MLM## Multracks_Rectangular Plotting WAB_30min_mean.FarmCPU## Multraits_Rectangular Plotting...(finished 13%)
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## Multracks_QQ Plotting WAB_30min_mean.GLM## Multracks_QQ Plotting WAB_30min_mean.MLM## Multracks_QQ Plotting WAB_30min_mean.FarmCPU## Multraits_QQ Plotting WAB_30min_mean.GLM
## Multraits_QQ Plotting WAB_30min_mean.MLM
## Multraits_QQ Plotting WAB_30min_mean.FarmCPUDensity Plot:
Let’s investigate the GWAS results for the traits
DM_NIRS, WAB_20min_mean, and
WAB_30min_mean, which were also considered for QTL
mapping.
DM_NIRS:
QQplot of GWAS models for DM_NIRS:
Manhattan Plot of GWAS models for DM_NIRS:
Statistically significant SNPs additive effects for DM_NIRS:
DM.NIRS.GLM.signals <- read.csv(file = "DM_NIRS.GLM_signals.csv", header = TRUE)
DM.NIRS.GLM.signals## SNP Chr Pos Effect SE DM_NIRS.GLM
## 1 chr01_31498756 1 31498756 -2.302833 0.3282478 2.999770e-11
## 2 chr01_31544211 1 31544211 2.218736 0.4140654 2.172779e-07
## 3 chr01_32001190 1 32001190 3.582134 0.5491636 4.937882e-10
## 4 chr01_32687089 1 32687089 2.628886 0.5077581 5.195382e-07
## 5 chr01_32694239 1 32694239 3.763603 0.6199308 5.755794e-09
## 6 chr01_33067627 1 33067627 -2.732186 0.4579702 1.002534e-08
## 7 chr01_33222617 1 33222617 2.597263 0.5047489 6.041679e-07
## 8 chr01_33244439 1 33244439 -6.914814 0.7013632 3.955384e-19
## 9 chr01_33250754 1 33250754 2.145098 0.4320971 1.410393e-06
## 10 chr01_33359326 1 33359326 3.946389 0.5631571 3.140926e-11
## 11 chr01_34671057 1 34671057 -2.364503 0.4626556 7.126068e-07
## 12 chr01_34963322 1 34963322 -4.121935 0.5974487 5.875996e-11
## 13 chr01_35951952 1 35951952 -3.170426 0.5464249 2.348455e-08
## 14 chr01_36342222 1 36342222 -2.455689 0.5095150 2.731641e-06
## 15 chr01_36478419 1 36478419 -2.892930 0.5553346 4.462114e-07
## 16 chr01_36754746 1 36754746 -2.809876 0.5562317 9.404264e-07
## 17 chr01_37058184 1 37058184 -5.225893 0.7809862 1.920962e-10
## 18 chr01_37225079 1 37225079 -3.919845 0.7402210 2.948970e-07
## 19 chr01_37330141 1 37330141 -2.767392 0.5609267 1.625234e-06
## 20 chr01_37460442 1 37460442 -3.726157 0.6906101 1.813031e-07
## 21 chr01_37601557 1 37601557 -2.808490 0.5505880 7.463756e-07DM.NIRS.MLM.signals <- read.csv(file = "DM_NIRS.MLM_signals.csv", header = TRUE)
DM.NIRS.MLM.signals## SNP Chr Pos Effect SE DM_NIRS.MLM
## 1 chr01_31498756 1 31498756 -2.208510 0.3771795 1.764330e-08
## 2 chr01_31544211 1 31544211 2.087751 0.4588235 8.957437e-06
## 3 chr01_32001190 1 32001190 3.574365 0.6162618 2.347935e-08
## 4 chr01_32687089 1 32687089 2.689982 0.5628273 3.256917e-06
## 5 chr01_32694239 1 32694239 3.736247 0.6910184 1.700010e-07
## 6 chr01_33067627 1 33067627 -2.939845 0.5160218 3.979067e-08
## 7 chr01_33222617 1 33222617 2.526538 0.5499744 7.407272e-06
## 8 chr01_33244439 1 33244439 -6.775264 0.8824841 5.627564e-13
## 9 chr01_33359326 1 33359326 3.853750 0.6491168 1.153116e-08
## 10 chr01_34671057 1 34671057 -2.633247 0.5268758 1.200412e-06
## 11 chr01_34963322 1 34963322 -4.463881 0.7115745 1.917253e-09
## 12 chr01_35951952 1 35951952 -3.380850 0.6413435 3.285720e-07
## 13 chr01_36478419 1 36478419 -3.093867 0.6441909 2.932458e-06
## 14 chr01_36754746 1 36754746 -3.023509 0.6439496 4.742356e-06
## 15 chr01_37058184 1 37058184 -5.380873 0.9206174 1.862806e-08
## 16 chr01_37225079 1 37225079 -4.162402 0.8547003 2.161373e-06
## 17 chr01_37330141 1 37330141 -3.041516 0.6597242 6.895983e-06
## 18 chr01_37460442 1 37460442 -3.790667 0.7788896 2.193667e-06
## 19 chr01_37601557 1 37601557 -2.864610 0.6270143 8.269063e-06DM.NIRS.FarmCPU.signals <- read.csv(file = "DM_NIRS.FarmCPU_signals.csv", header = TRUE)
DM.NIRS.FarmCPU.signals## SNP Chr Pos Effect SE DM_NIRS.FarmCPU
## 1 chr01_33244439 1 33244439 -4.794971 0.6203425 2.670378e-14
## 2 chr17_34503075 17 34503075 2.067476 0.5056101 5.395649e-06WAB_20min_mean:
QQplot of GWAS models for WAB_20min_mean:
Manhattan Plot of GWAS models for WAB_20min_mean:
Statistically significant SNPs additive effects for WAB_20min_mean:
WAB.20min.mean.GLM.signals <- read.csv(file = "WAB_20min_mean.GLM_signals.csv", header = TRUE)
WAB.20min.mean.GLM.signals## SNP Chr Pos Effect SE WAB_20min_mean.GLM
## 1 chr01_31498756 1 31498756 -0.5635170 0.1141130 1.591022e-06
## 2 chr01_31544211 1 31544211 0.6670322 0.1385579 2.801035e-06
## 3 chr01_32001190 1 32001190 1.0545634 0.1853813 4.197399e-08
## 4 chr01_32694239 1 32694239 1.0433538 0.2102696 1.426165e-06
## 5 chr01_33244439 1 33244439 -1.7697750 0.2523417 3.037074e-11
## 6 chr01_37058184 1 37058184 -1.2859028 0.2701833 3.582414e-06
## 7 chr01_37330141 1 37330141 -0.9254164 0.1853055 1.229816e-06
## 8 chr14_5161691 14 5161691 2.1478905 0.4691269 7.959640e-06WAB.20min.mean.MLM.signals <- read.csv(file = "WAB_20min_mean.MLM_signals.csv", header = TRUE)
WAB.20min.mean.MLM.signals## SNP Chr Pos Effect SE WAB_20min_mean.MLM
## 1 chr01_31498756 1 31498756 -0.5817123 0.1220193 3.437657e-06
## 2 chr01_32001190 1 32001190 1.0668656 0.2000251 2.431516e-07
## 3 chr01_32694239 1 32694239 1.0678013 0.2244055 3.579598e-06
## 4 chr01_33244439 1 33244439 -1.7769769 0.2876506 3.215380e-09
## 5 chr01_37330141 1 37330141 -0.9730553 0.2095997 5.988393e-06WAB.20min.mean.FarmCPU.signals <- read.csv(file = "WAB_20min_mean.FarmCPU_signals.csv", header = TRUE)
WAB.20min.mean.FarmCPU.signals## SNP Chr Pos Effect SE WAB_20min_mean.FarmCPU
## 1 chr01_20330444 1 20330444 -0.7509603 0.1662814 3.072326e-07
## 2 chr01_33244439 1 33244439 -1.6220867 0.1986882 4.265462e-16
## 3 chr10_24095256 10 24095256 1.4701395 0.2662429 6.968733e-09
## 4 chr12_4816670 12 4816670 -0.8644900 0.1596741 1.424908e-08
## 5 chr18_27608523 18 27608523 -0.8120224 0.1969613 8.006268e-06WAB_30min_mean:
QQplot of GWAS models for WAB_30min_mean:
Manhattan Plot of GWAS models for WAB_30min_mean:
Statistically significant SNPs additive effects for WAB_30min_mean:
WAB.30min.mean.GLM.signals <- read.csv(file = "WAB_30min_mean.GLM_signals.csv", header = TRUE)
WAB.30min.mean.GLM.signals## SNP Chr Pos Effect SE WAB_30min_mean.GLM
## 1 chr01_33244439 1 33244439 -3.669480 0.6742391 1.439545e-07
## 2 chr12_4816670 12 4816670 -2.470494 0.4783013 5.508152e-07WAB.30min.mean.MLM.signals <- read.csv(file = "WAB_30min_mean.MLM_signals.csv", header = TRUE)
WAB.30min.mean.MLM.signals## SNP Chr Pos Effect SE WAB_30min_mean.MLM
## 1 chr01_33244439 1 33244439 -3.649481 0.7495516 2.172507e-06WAB.30min.mean.FarmCPU.signals <- read.csv(file = "WAB_30min_mean.FarmCPU_signals.csv", header = TRUE)
WAB.30min.mean.FarmCPU.signals## SNP Chr Pos Effect SE WAB_30min_mean.FarmCPU
## 1 chr01_20330444 1 20330444 -1.883604 0.4359388 1.761652e-06
## 2 chr01_33244439 1 33244439 -3.543495 0.5045111 1.115087e-12
## 3 chr03_28198260 3 28198260 -8.197495 1.5238147 1.272805e-10
## 4 chr12_4816670 12 4816670 -1.741791 0.4171580 2.391657e-06
## 5 chr18_24294755 18 24294755 -1.913250 0.4632909 2.694021e-06